关于PCA和因子分析的真相

PCA是数据减少,没有任何关于数据来源的概率假设。挑选组件数量。伪造PCA的预测。因子分析可以做出更强大的概率假设,并提供更强大的预测结论 - 这可能是错误的。使用概率假设和/或预测来选择多少因素。因子分析作为图形因果模型的第一个玩具实例。旋转问题再一次带着感觉。因子模型和混合模型。因子模型和汤姆逊的采样模式:一个优秀的适合与几个因素的模型实际上是一个证据庞大的数量严重测量潜在变量
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1. The Truth about Principal Components and Factor Analysis 36-350, Data Mining 28 September 2009 Contents 1 The Truth about Principal Components Analysis 1 1.1 Convergence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Simulating with PCA . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 How Many Components? . . . . . . . . . . . . . . . . . . . . . . 3 2 The Truth about Factor Analysis 6 2.1 How Many Factors? . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 The Graphical Form of Factor Models . . . . . . . . . . . . . . . 8 2.3 The Rotation Problem Again . . . . . . . . . . . . . . . . . . . . 10 2.4 Factors or Mixtures? . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.5 The Thomson Sampling Model . . . . . . . . . . . . . . . . . . . 11 3 Advice 16 Having spent a great deal of time on the technicalities of principal compo- nents and factor analysis, we’ll wrap up by looking at their uses and abuses for understanding data; next time we’ll take up non-linear dimensionality reduc- tion.1 1 The Truth about Principal Components Anal- ysis Principal components tries to re-express the data as a sum of uncorrelated components. There are lots of other techniques which try to do similar things, like Fourier analysis, or wavelet decomposition. Things like Fourier analysis decompose the data into a sum of a fixed set of basis functions or basis vectors. This has the advantage of making results comparable across data sets, and of making the meaning of the components clear. So why ever do PCA rather than a Fourier transform? 1 Thanks to Dr. Moritz Heene for valuable discussion and references on factor analysis. 1

2. First, in some situations the idea of doing a Fourier transform is just embar- rassingly weird. For the states or cars data ets, we could number the features and take cosines of the feature numbers, etc., but it just seems crazy. No such embarrassment attends PCA. Second, when using a fixed set of components, there is no guarantee that a small number of components will give a good re- construction of the original data. PCA guarantees that the first q components will do a better (mean-square) job of reconstructing the original data than any other linear method using only q components. Third, it is good at preserving distances between the points — the component scores give the optimal linear multidimensional scaling (see section 3.7 of Principles of Data Mining). PCA gives us uncorrelated components, which are generally not independent components; for that you need independent component analysis (Stone, 2004). PCA looks for linear combinations of the original features; one could well do better by finding nonlinear combinations. Rather than directions in feature space, these would be curves or surfaces. PCA is purely a descriptive technique; in itself it makes no prediction about what future data will look like. 1.1 Convergence If the data come from IID samples of a distribution with covariance matrix V0 , then the sample covariance matrix V ≡ n1 XT X will converge on V0 as n → ∞. Since the principal components are functions of V (namely its eigenvectors), they will tend to converge as n grows2 . So, along with that additional assump- tion about the data-generating process, PCA does make a prediction: in the future, the principal components will look like they do now. 1.2 Simulating with PCA One can also try to turn PCA into a model which makes predictions about future data vectors more directly. The observed features are re-written in terms not just of the PCs but also of the projections along those PCs. One could try replacing those projection scores with random numbers and then transforming back into features to get new, simulated feature vectors. Remember that with q components, we get the component scores Y = Xw This turns the n × p matrix X into the n × q matrix Y by multiplying from the right with the p × q matrix w of eigenvectors. Because it is not a square matrix, w doesn’t, strictly speaking, have an inverse. However, suppose we look at wT : 2 There is a wrinkle if V has “degenerate” eigenvalues, i.e., two or more eigenvectors with 0 the same eigenvalue. Then any linear combination of those vectors is also an eigenvector, with the same eigenvalue. (Exercise: show this.) For instance, if V0 is the identity matrix, then every vector is an eigenvector, and PCA routines will return an essentially arbitrary collection of mutually perpendicular vectors. Generically, however, any arbitrarily small tweak to V0 will break the degeneracy. 2

3.the transpose is a q × p matrix, so wT w will be a q × q matrix. In fact, it will be the q-dimensional identity matrix. This is because the ij th entry in wT w is the dot product of the ith row of wT with the j th column of w, i.e., the dot product of two eigenvectors of V. But the eigenvectors are orthonormal, so this will be zero if i = j and 1 if i = j. So here is our simulation recipe. First, find the principal components matrix w and the component scores Y. Then replace Y with similar but random numbers, say J, and take JwT + where has the same distribution as the projection residuals.3 This synthetic data will have a similar covariance structure to the originals, but higher-order properties have been randomized.4 This kind of approach is sometimes used to create synthetic data for testing other algorithms, or to check whether the combination of components resembles the original in more qualtitative ways than just mean squared error.5 1.3 How Many Components? There are a couple of common ways of deciding how many principal components to use. 1. How many do you need? In homework 4, you saw that the accuracy with which tumors could be classified depended on how many principal components we used, and was peaked around q ≈ 12, falling off in either direction. If our goal is classifying tumors, this suggests that the right number of components to use is about 12. Of course, there is nothing which says that accuracy wouldn’t drop like a stone as soon as q < p. It’s always possible that the very smallest component, carrying the least variance, is also the only one carrying any information about what we actually want to make inferences about. 2. How much variance do you want? We can decide that we need at least a certain fraction of variance, and add components until we achieve it. How 3 One way to get “similar but random” values for J would be to fit a parametric distribution, such as the multivariate normal or the multivariate t, to the component scores. Another however is to just re-sample the component scores: draw a random value from the first column of Y, then a random value from the second, etc. Similarly to get the residuals: either ft a parametric distribution, or draw a residual vector at random from the empirical distribution. We will see more of this sort of resampling from the empirical distribution later, under the name of “bootstrapping”. 4 If the data are Gaussian, then the higher-order properties are fixed by the covariance, but that’s not generally the case. 5 Brian Whitman’s Eigenradio (eigenradio.media.mit.edu) would do something like this in real time to a couple of radio stations. Mostly it sounded mildly eerie, except when something recognizably like human music broke through, which sounded positively uncanny. The site is offline now, but see http://www.bagatellen.com/archives/interviews/000974.html for an interview where Whitman tries to explain it to a music blog. 3

4. someone comes to believe that they need 50% of the variance, rather than 40% or 60%, is honestly something I’ve never understood.6 3. How many eigenvalues are big? In some nice situations, there is a clear separation in size between the eigenvalues of V. A few eigenvalues are approximately as large as the largest eigenvalue, and all the others are at least an order of magnitude smaller. It’s then reasonable to hope that the large eigenvalues correspond to some components which dominate the data, and the small eigenvalues to stuff which is effectively small correc- tions to them.7 One can then make a clear separation between the large eigenvectors, and those which carry the noise components. Visually, one makes what’s called a scree plot, of eigenvalue magnitudes versus ranks, and hopes to see a gentle slope, followed by an abrupt cliff and then the “scree” of negligible eigenvalues. At least in my experience, however, such sharp separation is rare, leading to a lot of squinting and judgment calls. For instance, Figure 1 is the scree plot for the data from homework 4. How many components would you choose on this basis? What would you say if your boss wanted one more, or one less? 6 Except in some physical signal-processing contexts, where “variance” translates into “power transmitted by the signal”. 7 In physical contexts, this corresponds to distinguishing between a few slow, large- amplitude, macroscopic modes of motion, and many fast, small, microscopic noise modes. 4

5. Scree plot for Homework 4 600 500 400 eigenvalue 300 200 100 0 0 10 20 30 40 50 60 Index plot(prcomp(t(nci))$sdev^2,ylab="eigenvalue",main="Scree plot for Homework 4") Figure 1: Eigenvalue rank vs. magntidue for the principal components of the NCI gene-expression data set used in homework 4. 5

6.2 The Truth about Factor Analysis Recall the factor-analysis model: X = Fw + The factor-score matrix F is smaller than the data matrix X (n×q versus n×p), but Fw has nearly the same correlations as the original features. If we want to eliminate some dimensions while preserving correlations, then the factor scores are a good summary of the data. Many people also think of the factor model as a generative model, an account of how the data were produced in the first place. Seen this way, it is also a predictive model. Its prediction is that X ∼ N (0, wT w + Ψ) (1) Of course it might seem like it makes a more refined prediction, X|F ∼ N (F w, Ψ) (2) but the problem is that there is no way to guess at or estimate F until after we’ve seen X, at which point anyone can predict X perfectly. So the actual forecast is given by Eq. 1.8 Now, without going through the trouble of factor analysis, one could always just postulate that X ∼ N (0, V0 ) (3) and estimate V0 ; the maximum likelihood estimate of it is the observed covari- ance matrix V. The closer our estimate V is to V0 , the better our predictions. One way to think of factor analysis is that it looks for the maximum likelihood estimate, but constrained to matrices of the form wT w + Ψ. On the plus side, the constrained estimate has a faster rate of convergence. That is, both the constrained and unconstrained estimates are consistent and will converge on their optimal, population values as we feed in more and more data, but for the same amount of data the constrained estimate is probably closer to its limiting value. In other words, the constrained estimate wT w + Ψ has less variance than the unconstrained estimate V. On the minus side, maybe the true, population V0 just can’t be written in the form wT w + Ψ. Then we’re getting biased estimates of the covariance and the bias will not go away, even with infinitely many samples. Using factor analysis rather than just fitting a multivariate Gaussian means betting that either this bias is really zero, or that, with the amount of data on hand, the reduction in variance outweighs the bias. 8 A subtlety is that we might get to see some but not all of X, and use that to predict the rest. Say X = (X1 , X2 ), and we see X1 . Then we could, in principle, compute the conditional distribution of the factors, p(F |X1 ), and use that to predict X2 . Of course one could do the same thing using the correlation matrix, factor model or no factor model. 6

7. (I haven’t talked about estimated errors in the parameters of a factor model. The easiest way to obtain these is through either the jack-knife method — leave each observation out, re-estimate the model, and look at the distribution of re- estimates around the full-data estimate — or the bootstrap method — randomly re-sample the data, re-estimate, and again look at the distribution around the full-data estimate. We’ll see much more about both of these methods after the midterm, so just bear in mind that if you need to use factor analysis you can do this.) 2.1 How Many Factors? How many factors should we use? All the answers I gave for the how-many- principal-components question can be tried here, too, with the obvious modifi- cations. However, some other answers can also be given, using the fact that the factor model does make predictions, unlike PCA. 1. Log-likelihood ratio tests Sample covariances will almost never be exactly equal to population covariances. So even if the data comes from a model with q factors, we can’t expect the tetrad equations (or their multi-factor analogs) to hold exactly. The question then becomes whether the observed covariances are compatible with sampling fluctuations in a q-factor model, or are too big for that. We can tackle this question by using log likelihood ratio tests. The crucial observations are that a model with q factors is a special case of a model with q + 1 factors (just set a row of the weight matrix to zero), and that in the most general case, q = p, we can get any covariance matrix V into the form wT w. (Set Ψ = 0 and proceed as in the “principal factors” estimation method.) As a general rule, if θ is the maximum likelihood estimate in a restricted model with u parameters, and Θ is the MLE in a more general model with v > u parameters, containing the former as a special case, then 2[ (Θ) − (θ)] ∼ χ2 v − u when the data came from the small model. (Here is the log likelihood function.) The general regularity conditions needed for this to hold apply to Gaussian factor models, so we can test whether one factor is enough, two, etc. (Said another way, adding another factor never reduces the likelihood, but the equation tells us how much to expect the log-likelihood to go up when the new factor really adds nothing and is just over-fitting the noise.) The reasons for the χ2 distribution of the log likelihood ratio are a bit of a long story (involving the Fisher information matrix, etc.), but a rough version goes as follows. The big model has v −u more parameters than the small one; we can always arrange to write things so that the small model 7

8. is the big one, with those v − u parameters set to zero. The central limit theorem gives us Gaussian errors for each of the parameter estimates, but without bias, so they’re mean-zero Gaussians. Taylor-expand the log likelihood in the estimation errors: the first-order terms all cancel, leaving second order terms. (The 2 on the left-hand side began as the 1/2 in front of the quadratic terms in the Taylor series.) The square of a standard Gaussian is a χ2 random variable, so the log likelihood ratio is a sum of things proportional to χ21 s. In fact, the second derivatives in the Taylor expansion exactly cancel the standard deviations of the Gaussian estimation errors, leaving us v − u χ21 terms, or one χ2v−u . Determining q by getting the smallest one without a significant result in a likelihood ratio test is fairly traditional, but statistically messy.9 To raise a subject we’ll return to, if the true q > 1 and all goes well, we’ll be doing lots of hypothesis tests, and making sure this compound procedure works reliably is harder than controlling any one test. Perhaps more worrisomely, calculating the likelihood relies on distributional assumptions for the factor scores and the noises, which are hard to check for latent variables. 2. If you are comfortable with the distributional assumptions, use Eq. 1 to predict new data, and see which q gives the best predictions — for compa- rability, the predictions should be compared in terms of the log-likelihood they assign to the testing data. If genuinely new data is not available, use cross-validation. Comparative prediction, and especially cross-validation, seems to be some- what rare with factor analysis. If this is really the case, I couldn’t see why; perhaps I’m just not looking in the right places. 2.2 The Graphical Form of Factor Models One can represent the factor model as a graph like Figure 2. The square nodes stand for the features, which are observable, and the circles for the factors, which are not directly observable. The numbers beside the arrows are the factor loadings, taken from the matrix w. When the loading of a feature on a factor is zero, draw no arrow. Thus Xi2 = −0.75Fi1 + 0.34Fi2 + i2 . The correlations between variables can be worked out from the arrows: Xi1 and Xi2 have only the factor Fi1 in common, so their correlation is (0.87)(−0.75) = −0.65. On the other hand, Xi3 and Xi4 have two factors in common, and so their correlation is (0.13)(0.20) + (0.73)(0.10) = 0.099. Xi2 and Xi3 are conditionally independent, given Fi2 , because that is their only common factor. On the other hand, Fi1 and Fi2 are conditionally dependent given Xi2 , because knowing Xi2 tells us something about the value of −0.75Fi1 + 0.34Fi2 , and so about Fi1 and Fi2 . We will see later that there is a whole set of rules for deducing conditional independence relations from diagrams like this. 9 Suppose q is really 1, but by chance that gets rejected. Whether q = 2 gets rejected in term is not independent of this! 8

9. F1 F2 F3 0.87 -0.75 0.34 0.13 0.20 0.73 0.10 0.15 0.45 Xa Xb Xc Xd Xe Xf Figure 2: Graphical model form of a factor model. Circles stand for the unob- served factors, boxes for the observed features. Edges indicate non-zero entries in the factor loading matrix. This is because factor models are a special case of the broader class of graphical models, specifically a variety of linear Gaussian graphical model. A natural impulse, when looking at something like Figure 2, is to reify the factors and to treat the arrows causally: that is, to say that there really is some variable corresponding to each factor, and that changing the value of that variable will change the features. For instance, one might want to say that there is a real, physical variable corresponding to the factor Fi1 , and that increasing this by one standard deviation will, on average, increase Xi1 by 0.87 standard deviations, decrease Xi1 by 0.75 standard deviations, and do nothing to the other features. Moreover, changing any of the other factors has no effect on Xi1 . Sometimes all this is even right. How can we tell when it’s right? 9

10. G1 G2 F3 0.13 -0.45 0.86 -0.13 -0.69 0.02 -0.20 0.03 0.73 0.10 0.15 0.45 Xa Xb Xc Xd Xe Xf Figure 3: The model from Figure 2, after rotating the first two factors by 30 degrees around the third factor’s axis. The new factor loadings are rounded to two decimal places. 2.3 The Rotation Problem Again Consider the following matrix, call it R:   cos 30 − sin 30 0  sin 30 cos 30 0  0 0 1 Applied to a three-dimensional vector, this rotates it thirty degrees counter- clockwise around the vertical axis. If we apply R to the factor loading matrix of the model in the figure, we get the model in Figure 3. Now instead of Xi1 being correlated with the other variables only through one factor, it’s correlated through two factors, and Xi4 has incoming arrows from three factors. Because the transformation is orthogonal, the distribution of the observa- tions is unchanged. In particular, the fit of the new factor model to the data will be exactly as good as the fit of the old model. If we try to take this causally, however, we come up with a very different interpretation. The quality of the fit to the data does not, therefore, let us distinguish between these two models, and so these two stories about the causal structure of the data. 10

11. The rotation problem does not rule out the idea that checking the fit of a factor model would let us discover how many hidden causal variables there are. 2.4 Factors or Mixtures? Suppose we have two distributions with probability densities f0 (x) and f1 (x). Then we can define a new distribution which is a mixture of them, with density fα (x) = (1 − α)f0 (x) + αf1 (x), 0 ≤ α ≤ 1. The same idea works if we combine more than two distributions, so long as the sum of the mixing weights sum to one (as do α and 1 − α). We will look more later at mixture models, which provide a very flexible and useful way of representing complicated probability distributions. They are also a probabilistic, predictive alternative to the kind of clustering techniques we’ve seen before this: each distribution in the mixture is basically a cluster, and the mixing weights are the probabilities of drawing a new sample from the different clusters. I bring up mixture models here because there is a very remarkable result: any linear, Gaussian factor model with k factors is equivalent to some mixture model with k +1 clusters, in the sense that the two models have the same means and covariances (Bartholomew, 1987, pp. 36–38). Recall from Lecture 13 that the likelihood of a factor model depends on the data only through the correlation matrix. If the data really were generated by sampling from k + 1 clusters, then a model with k factors can match the covariance matrix very well, and so get a very high likelihood. This means it will, by the usual test, seem like a very good fit. Needless to say, however, the causal interpretations of the mixture model and the factor model are very different. The two may be distinguishable if the clusters are well-separated (by looking to see whether the data are unimodal or not), but that’s not exactly guaranteed. All of which suggests that factor analysis can’t really tell us whether we have k continuous hidden causal variables, or one discrete hidden variable taking k+1 values. 2.5 The Thomson Sampling Model We have been working with fewer factors than we have features. Suppose that’s not true. Suppose that each of our features is actually a linear combination of a lot of variables we don’t measure: q Xij = ηij + Aik Tkj = ηij + Ai · Tj (4) k=1 where q p. Suppose further that the latent variables Aik are totally inde- pendent of one another, but they all have mean 0 and variance 1; and that the noises ηij are independent of each other and of the Aik , with variance φj ; and the Tkj are independent of everything. What then is the covariance between Xia and Xib ? Well, because E [Xia ] = E [Xib ] = 0, it will just be the expectation of 11

12.the product of the features: E [Xia Xib ] = E (ηia + Ai · Ta )(ηib + Ai · Tb ) (5) = E [ηia ηib ] + E ηia Ai · Tb + E ηib Ai · Ta + E (Ai · Ta )(Ai · Tb(6) ) q q = 0+0+0+E Aik Tka Ail Tlb (7) k=1 l=1   = E Aik Ail Tka Tlb  (8) k,l = E [Aik Ail ] Tka Tlb (9) k,l = E [Aik Ail ] E [Tka Tlb ] (10) k,l q = E [Tka Tkb ] (11) k=1 where to get the last line I use the fact that E [Aik Ail ] = 1 if k = l and = 0 otherwise. If the coefficients T are fixed, then the last expectation goes away and we merely have the same kind of sum we’ve seen before, in the factor model. Instead, however, let’s say that the coefficients T are themselves random (but independent of A and η). For each feature Xia , we fix a proportion za |= between 0 and 1. We then set Tka ∼ Bernoulli(za ), with Tka Tlb unless k = l and a = b. Then E [Tka Tkb ] = E [Tka ] E [Tkb ] = za zb and E [Xia Xib ] = qza zb Of course, in the one-factor model, E [Xia Xib ] = wa wb So this random-sampling model looks exactly like the one-factor model with factor loadings proportional to za . The tetrad equation, in particular, will hold. Now, it doesn’t make a lot of sense to imagine that every time we make an observation we change the coefficients T randomly. Instead, let’s suppose that they are first generated randomly, giving values Tkj , and then we generate feature values according to Eq. 4. The covariance between Xia and Xib will be q k=1 Tka Tkb . But this is a sum of IID random values, so by the law of large numbers as q gets large this will become very close to qza zb . Thus, for nearly all choices of the coefficients, the feature covariance matrix should come very close to satisfying the tetrad equations and looking like there’s a single general factor. 12

13. In this model, each feature is a linear combination of a random sample of a huge pool of completely independent features, plus some extra noise specific to the feature.10 Precisely because of this, the features are correlated, and the pattern of correlations is that of a factor model with one factor. The appearance of a single common cause actually arises from the fact that the number of causes is immense, and there is no particular pattern to their influence on the features. The file thomson-model.R (on Blackboard) simulates the Thomson model. > tm = rthomson(50,11,500,50) > factanal(tm$data,1) The first command generates data from n = 50 items with p = 11 features and q = 500 latent variables. (The last argument controls the average size of the specific variances φj .) The result of the factor analysis is of course variable, de- pending on the random draws; my first attempt gave the proportion of variance associated with the factor as 0.391, and the p-value as 0.527. Repeating the simulation many times, one sees that the p-value is pretty close to uniformly distributed, which is what it should be if the null hypothesis is true (Figure 4). For fixed n, the distribution becomes closer to uniform the larger we make q. In other words, the goodness-of-fit test has little or no power against the alternative of the Thomson model. Modifying the Thomson model to look like multiple factors grows notation- ally cumbersome; the basic idea however is to use multiple pools of independently- sampled latent variables, and sum them: q1 q2 Xij = ηij + Aik Tkj + Bik Rkj + . . . k=1 k=1 where the Tkj coefficients are uncorrelated with the Rkj , and so forth. In ex- pectation, if there are r such pools, this exactly matches the factor model with r factors, and any particular realization is overwhelmingly likely to match if the q1 , q2 , . . . qr are large enough.11 It’s not feasible to estimate the T of the Thomson model in the same way that we estimate factor loadings, because q > p. This is not the point of considering the model, which is rather to make it clear that we actually learn very little about where the data come from when we learn that a factor model fits well. It 10 When Godfrey Thomson introduced this model in 1914, he used a slightly different pro- cedure to generate the coefficient Tkj . For each feature he drew a uniform integer between 1 and q, call it qj , and then sampled the integers from 1 to q without replacement until he had qj random numbers; these were the values of k where Tkj = 1. This is basically similar to what I describe, setting zj = qj /q, but a bit harder to analyze in an elementary way. — Thomson (1916), the original paper, includes what we would now call a simulation study of the model, where Thomson stepped through the procedure to produce simulated data, calcu- late the empirical correlation matrix of the features, and check the fit to the tetrad equations. Not having a computer, Thomson generated the values of Tkj with a deck of cards, and of the Aik and ηij by rolling 5220 dice. 11 A recent paper on the Thomson model (?) proposes just this modification to multiple factors and to Bernoulli sampling. However, I proposes this independently, in the fall 2008 version of these notes, about a year before their paper. 13

14. Sampling distribution of FA p-value under Thomson model 1.0 0.8 0.6 Empirical CDF 0.4 0.2 0.0 0.0 0.2 0.4 0.6 0.8 1.0 p value 200 replicates of 50 subjects each > plot(ecdf(replicate(200,factanal(rthomson(50,11,500,50)$data,1)$PVAL)), xlab="p value",ylab="Empirical CDF", main="Sampling distribution of FA p-value under Thomson model", sub="200 replicates of 50 subjects each") > abline(0,1,lty=2) Figure 4: Mimcry of the one-factor model by the Thomson model. The Thomson model was simulated 200 times with the parameters given above; each time, the simulated data was then fit to a factor model with one factor, and the p-value of the goodness-of-fit test extracted. The plot shows the empirical cumulative dis- tribution function of the p-values. If the null hypothesis were exactly true, then p ∼ Unif(0, 1), and the theoretical CDF would be the diagonal line (dashed). 14

15.could mean that the features arise from combining a small number of factors, or on the contrary from combining a huge number of factors in a random fashion. A lot of the time the latter is a more plausible-sounding story.12 For example, a common application of factor analysis is in marketing: you survey consumers and ask them to rate a bunch of products on a range of features, and then do factor analysis to find attributes which summarize the features. That’s fine, but it may well be that each of the features is influenced by lots of aspects of the product you don’t include in your survey, and the correlations are really explained by different features being affected by many of the same small aspects of the product. Similarly for psychological testing: answering any question is really a pretty complicated process involving lots of small processes and skills (of perception, several kinds of memory, problem- solving, attention, etc.), which overlap partially from question to question. 12 Thomson (1939) remains one of the most insightful books on factor analysis, though obviously there have been a lot of technical refinements since he wrote. It’s strongly recom- mended for anyone who plans to make much use of factor analysis. While out of print, used copies are reasonably plentiful and cheap, and at least one edition is free online (URL in the bibliography). 15

16.3 Advice Principal components is a pretty good thing to try if you need or want to do dimension reduction but aren’t sure what exactly to use. It’s got some reasonable mathematical properties, can often be interpreted, and runs fast (comparatively speaking). Factor analysis does not offer any general advantages over PCA when it comes to data reduction, except for preserving correlations. In practice, factor analysis is no better at accounting for variance than PCA, perhaps a bit worse and certainly more complicated (?). One or the other of them may work better, depending on your data and what you want to do with it. Factor analysis can also be used as a predictive model. This is possible because it fits a distribution to the data, and not because it actually gets at the underlying causal structure with any reliability or power. In both cases, the dimensions found by PCA and FA may be real features of the data, or they may just be more-or-less convenient fictions and summaries. That they are real is a hypothesis which these methods can suggest but for which they can provide only very weak evidence. This matters because ultimately we do data mining to discover knowledge on which we can act. It’s one thing if our action is just a prediction to help us adjust to the world, but it’s another if we go out and try to change the world based on how we think the different parts of it depend on each other. To do that well, we need to know what those parts really are. References Bartholomew, David J. (1987). Latent Variable Models and Factor Analysis. New York: Oxford University Press. Stone, James V. (2004). Independent Component Analysis: A Tutorial Intro- duction. Cambridge, Massachusetts: MIT Press. Thomson, Godfrey H. (1916). “A Hierarchy without a General Factor.” British Journal of Psychology, 8: 271–281. — (1939). The Factorial Analysis of Human Ability. Boston: Houghton Mifflin Company. 16