Properties of the Singular Value Decomposition for Efficient Data Clustering
SangKeun Lee, Monson H. Hayes III · IEEE Signal Processing Letters · 2004
We introduce some interesting properties of the singular value decomposition (SVD), and illustrate how they may be used in conjunction with the k-means algorithm for efficiently clustering a set of vectors. Specifically, we use the SVD to preprocess and sort the data vectors, and then use the k-means algorithm on the modified vectors. To illustrate the effectiveness of this approach, we compare it to the k-means algorithm without preprocessing and show that significant gains in clustering speed may be realized.