From 97dc527849b836703811acdbd6767685585099df Mon Sep 17 00:00:00 2001 From: Reza Zadeh Date: Wed, 1 Jan 2014 20:02:37 -0800 Subject: doc tweak --- docs/mllib-guide.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) (limited to 'docs/mllib-guide.md') diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index 08d6d74853..8c490eba69 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -215,17 +215,18 @@ Available algorithms for gradient descent: # Singular Value Decomposition Singular Value Decomposition for Tall and Skinny matrices. -Given an m x n matrix A, this will compute matrices U, S, V such that -A = U * S * V^T +Given an *m x n* matrix *A*, this will compute matrices *U, S, V* such that + +*A = U * S * V^T* There is no restriction on m, but we require n^2 doubles to fit in memory. Further, n should be less than m. -The decomposition is computed by first computing A^TA = V S^2 V^T, +The decomposition is computed by first computing *A^TA = V S^2 V^T*, computing svd locally on that (since n x n is small), from which we recover S and V. Then we compute U via easy matrix multiplication -as U = A * V * S^-1 +as *U = A * V * S^-1* Only singular vectors associated with singular values greater or equal to MIN_SVALUE are recovered. If there are k -- cgit v1.2.3