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Singular Value Decomposition (SVD) 2 1.1 Singular Vectors Contents 1 Singular Value Decomposition (SVD) 2 1.1 Singular Vectors . .3 1.2 Singular Value Decomposition (SVD) . .7 1.3 Best Rank k Approximations . .8 1.4 Power Method for Computing the Singular Value Decomposition . 11 1.5 Applications of Singular Value Decomposition . 16 1.5.1 Principal Component Analysis . 16 1.5.2 Clustering a Mixture of Spherical Gaussians . 16 1.5.3 An Application of SVD to a Discrete Optimization Problem . 22 1.5.4 SVD as a Compression Algorithm . 24 1.5.5 Spectral Decomposition . 24 1.5.6 Singular Vectors and ranking documents . 25 1.6 Bibliographic Notes . 27 1.7 Exercises . 28 1 1 Singular Value Decomposition (SVD) The singular value decomposition of a matrix A is the factorization of A into the product of three matrices A = UDV T where the columns of U and V are orthonormal and the matrix D is diagonal with positive real entries. The SVD is useful in many tasks. Here we mention some examples. First, in many applications, the data matrix A is close to a matrix of low rank and it is useful to find a low rank matrix which is a good approximation to the data matrix . We will show that from the singular value decomposition of A, we can get the matrix B of rank k which best approximates A; in fact we can do this for every k. Also, singular value decomposition is defined for all matrices (rectangular or square) unlike the more commonly used spectral decomposition in Linear Algebra. The reader familiar with eigenvectors and eigenvalues (we do not assume familiarity here) will also realize that we need conditions on the matrix to ensure orthogonality of eigenvectors. In contrast, the columns of V in the singular value decomposition, called the right singular vectors of A, always form an orthogonal set with no assumptions on A. The columns of U are called the left singular vectors and they also form an orthogonal set. A simple consequence of the orthogonality is that for a square and invertible matrix A, the inverse of A is VD−1U T , as the reader can verify. To gain insight into the SVD, treat the rows of an n × d matrix A as n points in a d-dimensional space and consider the problem of finding the best k-dimensional subspace with respect to the set of points. Here best means minimize the sum of the squares of the perpendicular distances of the points to the subspace. We begin with a special case of the problem where the subspace is 1-dimensional, a line through the origin. We will see later that the best-fitting k-dimensional subspace can be found by k applications of the best fitting line algorithm. Finding the best fitting line through the origin with respect to a set of points fxij1 ≤ i ≤ ng in the plane means minimizing the sum of the squared distances of the points to the line. Here distance is measured perpendicular to the line. The problem is called the best least squares fit. In the best least squares fit, one is minimizing the distance to a subspace. An alter- native problem is to find the function that best fits some data. Here one variable y is a function of the variables x1; x2; : : : ; xd and one wishes to minimize the vertical distance, i.e., distance in the y direction, to the subspace of the xi rather than minimize the per- pendicular distance to the subspace being fit to the data. Returning to the best least squares fit problem, consider projecting a point xi onto a line through the origin. Then 2 2 2 2 2 xi1 + xi2 + ··· +id = (length of projection) + (distance of point to line) : See Figure 1.1. Thus 2 2 2 2 2 (distance of point to line) = xi1 + xi2 + ··· +id − (length of projection) : To minimize the sum of the squares of the distances to the line, one could minimize 2 xj αj xi Min P α2 αi Equivalent to v Max P β2 βj βi Figure 1.1: The projection of the point xi onto the line through the origin in the direction of v n P 2 2 2 (xi1 + xi2 + ··· +id) minus the sum of the squares of the lengths of the projections of i=1 n P 2 2 2 the points to the line. However, (xi1 + xi2 + ··· +id) is a constant (independent of the i=1 line), so minimizing the sum of the squares of the distances is equivalent to maximizing the sum of the squares of the lengths of the projections onto the line. Similarly for best-fit subspaces, we could maximize the sum of the squared lengths of the projections onto the subspace instead of minimizing the sum of squared distances to the subspace. The reader may wonder why we minimize the sum of squared perpendicular distances to the line. We could alternatively have defined the best-fit line to be the one which minimizes the sum of perpendicular distances to the line. There are examples where this definition gives a different answer than the line minimizing the sum of perpendicular distances squared. [The reader could construct such examples.] The choice of the objective function as the sum of squared distances seems arbitrary and in a way it is. But the square has many nice mathematical properties - the first of these is the use of Pythagoras theorem above to say that this is equivalent to maximizing the sum of squared projections. We will see that in fact we can use the Greedy Algorithm to find best-fit k dimensional subspaces (which we will define soon) and for this too, the square is important. The reader should also recall from Calculus that the best-fit function is also defined in terms of least-squares fit. There too, the existence of nice mathematical properties is the motivation for the square. 1.1 Singular Vectors We now define the singular vectors of an n × d matrix A. Consider the rows of A as n points in a d-dimensional space. Consider the best fit line through the origin. Let v be a 3 th unit vector along this line. The length of the projection of ai; the i row of A, onto v is 2 jai · vj: From this we see that the sum of length squared of the projections is jAvj . The best fit line is the one maximizing jAvj2 and hence minimizing the sum of the squared distances of the points to the line. With this in mind, define the first singular vector, v1; of A, which is a column vector, as the best fit line through the origin for the n points in d-space that are the rows of A. Thus v1 = arg max jAvj: jvj=1 2 The value σ1 (A) = jAv1j is called the first singular value of A. Note that σ1 is the sum of the squares of the projections of the points to the line determined by v1. The greedy approach to find the best fit 2-dimensional subspace for a matrix A, takes v1 as the first basis vector for the 2-dimenional subspace and finds the best 2-dimensional subspace containing v1. The fact that we are using the sum of squared distances will again help. For every 2-dimensional subspace containing v1, the sum of squared lengths of the projections onto the subspace equals the sum of squared projections onto v1 plus the sum of squared projections along a vector perpendicular to v1 in the subspace. Thus, instead of looking for the best 2-dimensional subspace containing v1, look for a unit vector, call 2 it v2, perpendicular to v1 that maximizes jAvj among all such unit vectors. Using the same greedy strategy to find the best three and higher dimensional subspaces, defines v3; v4;::: in a similar manner. This is captured in the following definitions. There is no apriori guarantee that the greedy algorithm gives the best fit. But, in fact, the greedy algorithm does work and yields the best-fit subspaces of every dimension as we will show. The second singular vector, v2; is defined by the best fit line perpendicular to v1 v2 = arg max jAvj : v?v1;jvj=1 The value σ2 (A) = jAv2j is called the second singular value of A. The third singular vector v3 is defined similarly by v3 = arg max jAvj v?v1;v2;jvj=1 and so on. The process stops when we have found v1; v2;:::; vr as singular vectors and arg max jAvj = 0: v?v1;v2;:::;vr jvj=1 If instead of finding v1 that maximized jAvj and then the best fit 2-dimensional subspace containing v1, we had found the best fit 2-dimensional subspace, we might have 4 done better. This is not the case. We now give a simple proof that the greedy algorithm indeed finds the best subspaces of every dimension. Theorem 1.1 Let A be an n × d matrix where v1; v2;:::; vr are the singular vectors defined above. For 1 ≤ k ≤ r, let Vk be the subspace spanned by v1; v2;:::; vk. Then for each k, Vk is the best-fit k-dimensional subspace for A. Proof: The statement is obviously true for k = 1. For k = 2, let W be a best-fit 2- 2 2 dimensional subspace for A. For any basis w1; w2 of W , jAw1j + jAw2j is the sum of squared lengths of the projections of the rows of A onto W . Now, choose a basis w1; w2 of W so that w2 is perpendicular to v1.
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