History and Theory of Nonlinear Principal Component Analysis Jan de - - PowerPoint PPT Presentation

UCLA Department of Statistics

History and Theory

• f Nonlinear Principal Component Analysis

Jan de Leeuw February 11, 2011

Jan de Leeuw NLPCA History UCLA Department of Statistics

Abstract

Relationships between Multiple Correspondence Analysis (MCA) and Nonlinear Principal Component Analysis (NLPCA), which is defined as PCA with Optimal Scaling (OS), are discussed. We review the history of NLPCA. We discuss forms of NLPCA that have been proposed over the years:

Shepard-Kruskal- Breiman-Friedman-Gifi PCA with optimal scaling, Aspect Analysis of correlations, Guttman’s MSA, Logit and Probit PCA of binary data, and Logistic Homogeneity Analysis.

Since I am trying to summarize 40+ years of work, the presentation will be rather dense.

Jan de Leeuw NLPCA History UCLA Department of Statistics

Linear PCA

History

(Linear) Principal Components Analysis (PCA) is sometimes attributed to Hotelling (1933), but that is surely incorrect. The equations for the principal axes of quadratic forms and surfaces, in various forms, were known from classical analytic geometry (notably from work by Cauchy and Jacobi in the mid 19th century). There are some modest beginnings in Galton’s Natural Inheritance of 1889, where the principal axes are connected for the first time with the “correlation ellipsoid". There is a full-fledged (although tedious) discussion of the technique in Pearson (1901), and there is a complete application (7 physical traits of 3000 criminals) in MacDonell (1902), by a Pearson co-worker. There is proper attribution in: Burt, C., Alternative Methods of Factor Analysis and their Relations to Pearson’s Method of “Principle Axes”, Br.

• J. Psych., Stat. Sec., 2 (1949), pp. 98-121.

Jan de Leeuw NLPCA History UCLA Department of Statistics

Linear PCA

How To

Hotelling’s introduction of PCA follows the now familiar route of making successive orthogonal linear combinations with maximum variance. He does this by using Power iterations (without reference), discussed in 1929 by Von Mises and Pollaczek-Geiringer. Pearson, following Galton, used the correlation ellipsoid throughout. This seems to me the more basic approach. He cast the problem in terms of finding low-dimensional subspaces (lines and planes) of best (least squares) fit to a cloud of points, and connects the solution to the principal axes of the correlation ellipsoid. In modern notation, this means minimizing SSQ(Y − XB′) over n × r matrices X and m × r matrices B. For r = 1 this is the best line, etc.

Jan de Leeuw NLPCA History UCLA Department of Statistics

Correspondence Analysis

History

Simple Correspondence Analysis (CA) of a bivariate frequency table was first discussed, in fairly rudimentary form, by Pearson (1905), by looking at transformations linearizing regressions. See De Leeuw, On the Prehistory of Correspondence Analysis, Statistica Neerlandica, 37, 1983, 161–164. This was taken up by Hirshfeld (Hartley) in 1935, where the technique was presented in a fairly complete form (to maximize correlation and decompose contingency). This approach was later adopted by Gebelein, and by Renyi and his students in their study of maximal correlation.

Jan de Leeuw NLPCA History UCLA Department of Statistics

Correspondence Analysis

History

In the 1938 edition of Statistical Methods for Research Workers Fisher scores a categorical variable to maximize a ratio of variances (quadratic forms). This is not quite CA, because it is presented in an (asymmetric) regression context. Symmetric CA and the reciprocal averaging algorithm are discussed, however, in Fisher (1940) and applied by his co-worker Maung (1941a,b). In the early sixties the chi-square metric, relating CA to metric multidimensional scaling (MDS), with an emphasis on geometry and plotting, was introduced by Benzécri (thesis of Cordier, 1965).

Jan de Leeuw NLPCA History UCLA Department of Statistics

Multiple Correspondence Analysis

History

Different weighting schemes to combine quantitative variables to an index that optimizes some variance-based discrimination or homogeneity criterion were proposed in the late thirties by Horst (1936), by Edgerton and Kolbe (1936), and by Wilks (1938). The same idea was applied to quantitative variables in a seminal paper by Guttman (1941), that presents, for the first time, the equations defining Multiple Correspondence Analysis (MCA). The equations are presented in the form of a row-eigen (scores), a column-eigen (weights), and a singular value (joint) problem. The paper introduces the “codage disjonctif complet” as well as the “Tableau de Burt”, and points out the connections with the chi-square metric. There is no geometry, and the emphasis is on constructing a single

• scale. In fact Guttman warns against extracting and using additional

eigen-pairs.

Jan de Leeuw NLPCA History UCLA Department of Statistics

Multiple Correspondence Analysis

Further History

In Guttman (1946) scale or index construction was extended to paired comparisons and ranks. In Guttman (1950) it was extended to scalable binary items. In the fifties and sixties Hayashi introduced the quantification techniques

• f Guttman in Japan, where they were widely disseminated through the

work of Nishisato. Various extensions and variations were added by the Japanese school. Starting in 1968, MCA was studied as a form of metric MDS by De Leeuw. Although the equations defining MCA were the same as those defining PCA, the relationship between the two remained problematic. These problems are compounded by “horse shoes” or the “effect Guttman”, i.e. artificial curvilinear relationships between successive dimensions (eigenvectors).

Jan de Leeuw NLPCA History UCLA Department of Statistics

Nonlinear PCA

What ?

PCA can be made non-linear in various ways.

1

First, we could seek indices which discriminate maximally and are non-linear combinations of variables. This generalizes the weighting approach (Hotelling).

2

Second, we could find nonlinear combinations of components that are close to the observed variables. This generalizes the reduced rank approach (Pearson).

3

Third, we could look for transformations of the variables that optimize the linear PCA fit. This is known (term of Darrell Bock) as the optimal scaling (OS) approach.

Jan de Leeuw NLPCA History UCLA Department of Statistics

Nonlinear PCA

Forms

The first approach has not been studied much, although there are some relations with Item Response Theory. The second approach is currently popular in Computer Science, as “nonlinear dimension reduction”. I am currently working on a polynomial version, but there is not unified theory, and the papers are usually of the “‘well, we could also do this” type familiar from cluster analysis. The third approach preserves many of the properties of linear PCA and can be connected with MCA as well. We shall follow its history and discuss the main results.

Jan de Leeuw NLPCA History UCLA Department of Statistics

Nonlinear PCA

PCA with OS

Guttman observed in 1959 that if we require that the regression between monotonically transformed variables are linear, then the transformations are uniquely defined. In general, however, we need approximations. The loss function for PCA-OS is SSQ(Y − XB′), as before, but now we minimize over components X, loadings B, and transformations Y. Transformations are defined column-wise (over variables) and belong to some restricted class (monotone, step, polynomial, spline). Algorithms often are of the alternating least squares type, where optimal transformation and low-rank matrix approximation are alternated until convergence.

Jan de Leeuw NLPCA History UCLA Department of Statistics

PCA-OS

History of programs

Shepard and Kruskal used the monotone regression machinery of non-metric MDS to construct the first PCA-OS programs around 1962. The paper describing the technique was not published until 1975. Around 1970 versions of PCA-OS (sometimes based on Guttman’s rank image principle) were developed by Lingoes and Roskam. In 1973 De Leeuw, Young, and Takane started the ALSOS project, with resulted in PRINCIPALS (published in 1978), and PRINQUAL in SAS. In 1980 De Leeuw (with Heiser, Meulman, Van Rijckevorsel, and many

• thers) started the Gifi project, which resulted in PRINCALS, in SPSS

CATPCA, and in the R package homals by De Leeuw and Mair (2009). In 1983 Winsberg and Ramsay published a PCA-OS version using monotone spline transformations. In 1987 Koyak, using the ACE smoothing methodology of Breiman and Friedman (1985), introduced mdrace.

Jan de Leeuw NLPCA History UCLA Department of Statistics

PCA/MCA

The Gifi Project

The Gifi project followed the ALSOS project. It has or had as its explicit goals:

1

Unify a large class of multivariate analysis methods by combining a single loss function, parameter constraints (as in MDS), and ALS algorithms.

2

Give a very general definition of component analysis (to be called homogeneity analysis) that would cover CA, MCA, linear PCA, nonlinear PCA, regression, discriminant analysis, and canonical analysis.

3

Write code and analyze examples for homogeneity analysis.

Jan de Leeuw NLPCA History UCLA Department of Statistics

Gifi

Loss of Homogeneity

The basic Gifi loss function is

σ(X,Y) =

m

∑

j=1

SSQ(X − GjYj). The n × kj matrices Gj are the data, coded as indicator matrices (or dummies). Alternatively, Gj can be a B-spline basis. Also, Gj can have zero rows for missing data. X is an n × p matrix of object scores, satisfying the normalization conditions X′X = I. Yj are kj × p matrices of category quantifications. There can be rank, level and additivity constraints on the Yj.

Jan de Leeuw NLPCA History UCLA Department of Statistics

Gifi

ALS

The basic Gifi algorithm alternates

1

X (k) = ORTH(∑m

j=1 GjY (k) j

).

2

Y (k+1)

j

= argmin

Yj∈Yj

tr (ˆ Y (k+1)

j

− Yj)′Dj(ˆ

Y (k+1)

j

− Yj).

We use the following notation. Superscript (k) is used for iterations. ORTH() is any orthogonalization method such as QR, Gram-Schmidt,

• r SVD.

Dj = G′

jGj are the marginals.

ˆ

Y (k+1)

j

= D−1

j

G′

jX (k) are the category centroids.

The constraints on Yj are written as Yj ∈ Yj.

Jan de Leeuw NLPCA History UCLA Department of Statistics

Gifi

Star Plots

Let’s look at some movies. GALO: 1290 students, 4 variables. We show both MCA and NLPCA. Senate: 100 senators, 20 votes. Since the variables are binary, MCA = NLPCA.

Jan de Leeuw NLPCA History UCLA Department of Statistics

• bjplot galo

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Jan de Leeuw NLPCA History UCLA Department of Statistics

• bjplot senate

Sessions Shelby Murkowski Stevens Kyl McCain Hutchinson Lincoln Boxer Feinstein Allard Campbell Dodd Lieberman Biden Carper Graham Nelson Cleland Miller Akaka Inouye Craig Crapo Durbin Fitzgerald Bayh Lugar Grassley Harkin Brownback Roberts Bunning McConnell Breaux Landrieu Collins Snowe Mikulski Sarbanes Kennedy Kerry Levin Stabenow Dayton Wellstone Cochran Lott Bond Carnahan Baucus Burns Hagel Nelson1 Ensign Reid Gregg Smith1 Corzine Torricelli Bingaman Domenici Clinton Schumer Edwards Helms Conrad Dorgan DeWine Voinovich Inhofe Nickles Smith Wyden Santorum Specter Chafee Reed Hollings Thurmond Daschle Johnson Frist Thompson Gramm Hutchison Bennett Hatch Jeffords Leahy Allen Warner Cantwell Murray Byrd Rockefeller Feingold Kohl Enzi Thomas

Jan de Leeuw NLPCA History UCLA Department of Statistics

Jan de Leeuw NLPCA History UCLA Department of Statistics

Gifi

Single Variables

If there are no constraints on the Yj homogeneity analysis is MCA. We will not go into additivity constraints, because they take us from PCA and MCA towards regression and canonical analysis. See the homals paper and package. A single variable has constraints Yj = zja′

j, i.e. category quantifications

are of rank one. In a given analysis some variables can be single while

• ther can be multiple (unconstrained). More generally, there can be rank

constraints on the Yj. This can be combined with level constraints on the single quantifications zj, which can be numerical, polynomial, ordinal, or nominal. If all variables are single homogeneity analysis is NLPCA (i.e. PCA-OS). This relationship follows from the form of the loss function.

Jan de Leeuw NLPCA History UCLA Department of Statistics

Gifi

Multiple Variables

There is another relationship, which is already implicit in Guttman (1941). If we transform the variables to maximize the dominant eigenvalue of the correlation matrix, then we find both the first MCA dimension and the one-dimensional nominal PCA solution. But there are deeper relations between MCA and NLPCA. These were developed in a series of papers by De Leeuw and co-workers, starting in

• 1980. Their analysis also elucidates the “Effect Guttman”.

These relationships are most easily illustrated by performing an MCA of a continuous standardized multivariate normal, say on m variables, analyzed in the form of a Burt Table (with doubly-infinite subtables). Suppose the correlation matrix of this distribution is the m × m matrix R = {rjℓ}.

Jan de Leeuw NLPCA History UCLA Department of Statistics

Gifi

Multinormal MCA

Suppose I = R[0],R = R[1],R[2],... is the infinite sequence of Hadamard (elementwise) powers of R. Suppose λ [s]

j

are the m eigenvalues of R[s] and y[s]

j

are the corresponding eigenvectors. The eigenvalues of the MCA solution are the m ×∞ eigenvalues λ [s]

j

. The MCA eigenvector corresponding to λ [s]

j

consists of the m functions y[s]

jℓ Hs, with Hs the sth normalized Hermite polynomial.

An MCA eigenvector consists of m linear transformations, or m quadratic transformations, and so on. There are m linear eigenvectors, m quadratic eigenvectors, and so on.

Jan de Leeuw NLPCA History UCLA Department of Statistics

Gifi

Multinormal MCA

The same theory applies to what Yule (1900) calls “strained multinormal” variables zj, in which there exists diffeomorphisms φj such that φj(zj) are jointly multinormal (an example are Gaussian copulas). And the same theory also applies, except for the polynomial part, when separate transformations of the variables exists that linearize all bivariate regressions (this generalizes a result of Pearson from 1905). Under all these scenarios, MCA solutions are NLPCA solutions, and vice versa. With the provision that NLPCA solutions are always selected from the same R[s], while MCA solutions come from all R[s]. Also, generally, the dominant eigenvalue is λ [1]

1

and the second largest

• ne is either λ [2]

1

• r λ [1]

2 . In the first case we have a horseshoe.

Jan de Leeuw NLPCA History UCLA Department of Statistics

Gifi

Bilinearizability

The “joint bilinearizibility” also occurs (trivially) if m = 2, i.e. in CA, and if kj = 2 for all j, i.e. for binary variables. If there is joint linearizability then the joint first-order asymptotic normal distribution of the induced correlation coefficients does not depend on the standard errors of the computed optimal transformations (no matter if they come from MCA or NLPCA or any other OS method). There is additional horseshoe theory, due mostly to Schriever (1986), that uses the Krein-Gantmacher-Karlin theory of total positivity. It is not based on families of orthogonal polynomials, but on (higher-order) order relations. This was, once again, anticipated by Guttman (1950) who used finite difference equations to derive the horseshoe MCA/NLPCA for the binary items defining a perfect scale.

Jan de Leeuw NLPCA History UCLA Department of Statistics

More

Pavings

If we have a mapping of n objects into Rp then a categorical variable can be used to label the objects. The subsets corresponding to the categories of the variables are supposed to be homogeneous. This can be formalized either as being small or as being separated by lines or curves. There are many ways to quantify this in loss functions. MCA (multiple variables, star plots) tends to think small (within vs between), NLPCA tends to think separable. Guttman’s MSA defines outer and inner points of a category. An outer point is a closest point for any point not in the category. The closest outer point for an inner point should belong to the same category as the inner point. This is a nice “topological” way to define separation, but it is hard to quantify.

Jan de Leeuw NLPCA History UCLA Department of Statistics

More

Aspects

The aspect approach (De Leeuw and Mair, JSS, 2010, using theory from De Leeuw, 1990) goes back to Guttman’s 1941 original motivation. An aspect is any function of all correlations (and/or the correkation ratios) between m transformed variables. Now choose the transformations/quantifications such that the aspect is

• maximized. We use majorization to turns this into a sequence of least

squares problems. For MCA the aspect is the largest eigenvalue, for NLPCA it is the sum of the largest p eigenvalues. Determinants, multiple correlations, canonical correlations can also be used as aspects. Or: the sum of the differences of the correlation ratios and the squared correlation coefficients. Multinormal, strained multinormal, and bilinearizability theory applies to all aspects.

Jan de Leeuw NLPCA History UCLA Department of Statistics

More

Logistic

Instead of using least squares throughout, we can build a similar system using logit or probit log likelihoods. This is in the development stages. The basic loss function, corresponding to the Gifi loss function, is

n

∑

i=1 m

∑

j=1 kj

∑

ℓ=1

gijℓ log exp{−φ(xi,yjℓ)}

∑

kj

ν=1 exp{−φ(xi,yjν)}

where the data are indicators, as before. The function φ can be distance, squared distance, or negative inner product. This emphasizes separation, because we want the xi closest to the yjℓ for which gijℓ = 1. We use majorization to turn this into a sequence of reweighted least squares MDS or PCA problems.

Jan de Leeuw NLPCA History UCLA Department of Statistics