importance of PCA or SVD in machine learning

The Singular Value Decomposition is often used to approximate a matrix X by a low rank matrix X_lr:

1. Compute the SVD X = U D V^T.
2. Form the matrix D' by keeping the k largest singular values and setting the others to zero.
3. Form the matrix X_lr by X_lr = U D' V^T.

The matrix X_lr is then the best approximation of rank k of the matrix X, for the Frobenius norm (the equivalent of the l2-norm for matrices). It is computationally efficient to use this representation, because if your matrix X is n by n and k << n, you can store its low rank approximation with only (2n + 1)k coefficients (by storing U, D' and V).

This was often used in matrix completion problems (such as collaborative filtering) because the true matrix of user ratings is assumed to be low rank (or well approximated by a low rank matrix). So, you wish to recover the true matrix by computing the best low rank approximation of your data matrix. However, there are now better ways to recover low rank matrices from noisy and missing observations, namely nuclear norm minimization. See for example the paper The power of convex relaxation: Near-optimal matrix completion by E. Candes and T. Tao.

(Note: the algorithms derived from this technique also store the SVD of the estimated matrix, but it is computed differently).

SVD is not used to normalize the data, but to get rid of redundant data, that is, for dimensionality reduction. For example, if you have two variables, one is humidity index and another one is probability of rain, then their correlation is so high, that the second one does not contribute with any additional information useful for a classification or regression task. The eigenvalues in SVD help you determine what variables are most informative, and which ones you can do without.

The way it works is simple. You perform SVD over your training data (call it matrix A), to obtain U, S and V*. Then set to zero all values of S less than a certain arbitrary threshold (e.g. 0.1), call this new matrix S'. Then obtain A' = US'V* and use A' as your new training data. Some of your features are now set to zero and can be removed, sometimes without any performance penalty (depending on your data and the threshold chosen). This is called k-truncated SVD.

SVD doesn't help you with sparsity though, only helps you when features are redundant. Two features can be both sparse and informative (relevant) for a prediction task, so you can't remove either one.

Using SVD, you go from n features to k features, where each one will be a linear combination of the original n. It's a dimensionality reduction step, just like feature selection is. When redundant features are present, though, a feature selection algorithm may lead to better classification performance than SVD depending on your data set (for example, maximum entropy feature selection). Weka comes with a bunch of them.

See: http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Dimensionality_Reduction/Singular_Value_Decomposition

https://stats.stackexchange.com/questions/33142/what-happens-when-you-apply-svd-to-a-collaborative-filtering-problem-what-is-th