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Unsupervised and Supervised Principal Component Analysis: Tutorial Unsupervised and Supervised Principal Component Analysis: Tutorial Benyamin Ghojogh [email protected] Department of Electrical and Computer Engineering, Machine Learning Laboratory, University of Waterloo, Waterloo, ON, Canada Mark Crowley [email protected] Department of Electrical and Computer Engineering, Machine Learning Laboratory, University of Waterloo, Waterloo, ON, Canada Abstract space and manifold learning (Friedman et al., 2009). This This is a detailed tutorial paper which explains method, which is also used for feature extraction (Ghojogh the Principal Component Analysis (PCA), Su- et al., 2019b), was first proposed by (Pearson, 1901). In or- pervised PCA (SPCA), kernel PCA, and kernel der to learn a nonlinear sub-manifold, kernel PCA was pro- SPCA. We start with projection, PCA with eigen- posed, first by (Scholkopf¨ et al., 1997; 1998), which maps decomposition, PCA with one and multiple pro- the data to high dimensional feature space hoping to fall on jection directions, properties of the projection a linear manifold in that space. matrix, reconstruction error minimization, and The PCA and kernel PCA are unsupervised methods for we connect to auto-encoder. Then, PCA with sin- subspace learning. To use the class labels in PCA, super- gular value decomposition, dual PCA, and ker- vised PCA was proposed (Bair et al., 2006) which scores nel PCA are covered. SPCA using both scor- the features of the X and reduces the features before ap- ing and Hilbert-Schmidt independence criterion plying PCA. This type of SPCA were mostly used in bioin- are explained. Kernel SPCA using both direct formatics (Ma & Dai, 2011). and dual approaches are then introduced. We Afterwards, another type of SPCA (Barshan et al., 2011) cover all cases of projection and reconstruction was proposed which has a very solid theory and PCA is of training and out-of-sample data. Finally, some really a special case of it when we the labels are not used. simulations are provided on Frey and AT&T face This SPCA also has dual and kernel SPCA. datasets for verifying the theory in practice. The PCA and SPCA have had many applications for ex- ample eigenfaces (Turk & Pentland, 1991a;b) and kernel 1. Introduction eigenfaces (Yang et al., 2000) for face recognition and de- tecting orientation of image using PCA (Mohammadzade Assume we have a dataset of instances or data points n et al., 2017). There exist many other applications of PCA f(xi; yi)gi=1 with sample size n and dimensionality xi 2 d ` n and SPCA in the literature. In this paper, we explain the R and yi 2 R . The fxigi=1 are the input data to n theory of PCA, kernel SPCA, SPCA, and kernel SPCA and the model and the fyigi=1 are the observations (labels). d×n `×n provide some simulations for verifying the theory in prac- We define R 3 X := [x1;:::; xn] and R 3 arXiv:1906.03148v1 [stat.ML] 1 Jun 2019 tice. Y := [y1;:::; yn]. We can also have an out-of-sample d data point, xt 2 R , which is not in the training set. If nt 2. Principal Component Analysis there are nt out-of-sample data points, fxt;ig1 , we define d×nt 2.1. Projection Formulation R 3 Xt := [xt;1;:::; xt;nt ]. Usually, the data points exist on a subspace or sub-manifold. Subspace or manifold 2.1.1. A PROJECTION POINT OF VIEW learning tries to learn this sub-manifold (Ghojogh et al., d Assume we have a data point x 2 R . We want to project 2019b). this data point onto the vector space spanned by p vectors Principal Component Analysis (PCA) (Jolliffe, 2011) is a fu1;:::; upg where each vector is d-dimensional and usu- very well-known and fundamental linear method for sub- ally p d. We stack these vectors column-wise in matrix d×p U = [u1;:::; up] 2 R . In other words, we want to project x onto the column space of U, denoted by Col(U). The projection of x 2 Rd onto Col(U) 2 Rp and then its Unsupervised and Supervised Principal Component Analysis: Tutorial 2 Figure 1. The residual and projection onto the column space of U. representation in the Rd (its reconstruction) can be seen as a linear system of equations: d R 3 xb := Uβ; (1) Figure 2. The principal directions P1 and P2 for (a) non-centered p and (b) centered data. As can be seen, the data should be centered where we should find the unknown coefficients β 2 R . for PCA. If the x lies in the Col(U) or spanfu1;:::; upg, this linear system has exact solution, so xb = x = Uβ. However, if x does not lie in this space, there is no any solution β for So, we have: x = Uβ and we should solve for projection of x onto > xb = Π x = UU x: (6) Col(U) or spanfu1;:::; upg and then its reconstruction. In other words, we should solve for Eq. (1). In this case, xb and x are different and we have a residual: 2.1.2. PROJECTION AND RECONSTRUCTION IN PCA The Eq. (6) can be interpreted in this way: The U >x r = x − x = x − Uβ; (2) > b projects x onto the row space of U, i.e., Col(U ) (pro- which we want to be small. As can be seen in Fig.1, the jection onto a space spanned by d vectors which are p- dimensional). We call this projection, “projection onto the smallest residual vector is orthogonal to Col(U); therefore: PCA subspace”. It is “subspace” because we have p ≤ d x − Uβ ? U =) U >(x − Uβ) = 0; where p and d are dimensionality of PCA subspace and > > > x U(U x) =) β = (U U)−1U x: (3) the original , respectively. Afterwards, projects the projected data back onto the column space of U, i.e., It is noteworthy that the Eq. (3) is also the formula of co- Col(U) (projection onto a space spanned by p vectors efficients in linear regression (Friedman et al., 2009) where which are d-dimensional). We call this step “reconstruc- the input data are the rows of U and the labels are x; how- tion from the PCA” and we want the residual between x ever, our goal here is different. Nevertheless, in Section and its reconstruction xb to be small. n 2.4, some similarities of PCA and regression will be intro- If there exist n training data points, i.e., fxigi=1, the pro- duced. jection of a training data point x is: Plugging Eq. (3) in Eq. (1) gives us: p > R 3 x := U x˘; (7) > −1 > e xb = U(U U) U x: where: We define: d 3 x˘ := x − µ ; (8) d×d > −1 > R x R 3 Π := U(U U) U ; (4) is the centered data point and: as “projection matrix” because it projects x onto Col(U) n (and reconstructs back). Note that Π is also referred to as 1 X d 3 µ := x ; (9) the “hat matrix” in the literature because it puts a hat on top R x n i of x. i=1 If the vectors fu1;:::; upg are orthonormal (the matrix U is the mean of training data points. The reconstruction of is orthogonal), we have U > = U −1 and thus U >U = I. a training data point x after projection onto the PCA sub- Therefore, Eq. (4) is simplified: space is: > d > Π = UU : (5) R 3 xb := UU x˘ + µx = Uxe + µx; (10) Unsupervised and Supervised Principal Component Analysis: Tutorial 3 d×nt where the mean is added back because it was removed be- If we consider the nt out-of-sample data points, R 3 fore projection. Xt = [xt;1;:::; xt;nt ], all together, the projection and re- Note that in PCA, all the data points should be centered, construction of them are: i.e., the mean should be removed first. The reason is shown p×nt > ˘ in Fig.2. In some applications, centering the data does not R 3 Xft = U Xt; (18) make sense. For example, in natural language processing, d×nt > ˘ R 3 Xct = UU Xt + µx = UXft + µx; (19) the data are text and centering the data makes some nega- tive measures which is non-sense for text. Therefore, data respectively, where: is not sometimes centered and PCA is applied on the non- d×nt ˘ centered data. This method is called Latent Semantic In- R 3 Xt := Xt − µx: (20) dexing (LSI) or Latent Semantic Analysis (LSA) (Dumais, 2004). 2.2. PCA Using Eigen-Decomposition If we stack the n data points column-wise in a matrix X = 2.2.1. PROJECTION ONTO ONE DIRECTION d×n [x1;:::; xn] 2 R , we first center them: In Eq. (10), if p = 1, we are projecting x onto only one vector u and reconstruct it. If we ignore adding the mean d×n ˘ R 3 X := XH = X − µx; (11) back, we have: where X˘ = [x˘ ;:::; x˘ ] = [x − µ ;:::; x − µ ] is the > 1 n 1 x n x xb = uu x˘: centered data and: The squared length (squared `2-norm) of this reconstructed n×n > R 3 H := I − (1=n)11 ; (12) vector is: is the centering matrix. See AppendixA for more details 2 > 2 > > > jjxbjj2 = jjuu x˘jj2 = (uu x˘) (uu x˘) about the centering matrix. > > > (a) > > (b) > > The projection and reconstruction, Eqs. (7) and (10), for = x˘ u u u u x˘ = x˘ u u x˘ = u x˘ x˘ u; (21) |{z} the whole training data are: 1 p×n > ˘ where (a) is because u is a unit (normal) vector, i.e., R 3 Xf := U X; (13) > 2 > > u u = jjujj2 = 1, and (b) is because x˘ u = u x˘ 2 R.
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