QUIC-SVD: Fast SVD Using Cosine Trees

The Singular Value Decomposition is a key operation in many machine learning methods. Its computational cost, however, makes it unscalable and impractical for applications involving large datasets or real-time responsiveness, which are becoming increasingly common. We present a new method, QUIC-SVD, for fast approximation of the whole-matrix SVD based on a new sampling mechanism called the cosine tree. Our empirical tests show speedups of several orders of magnitude over exact SVD. Such scalability should enable QUIC-SVD to accelerate and enable a wide array of SVD-based methods and applications.

• Type: Conference
• Year: 2008
• Where: NIPS
• Authors: Michael P. Holmes, Alexander G. Gray, Charles Lee Isbell Jr.
• Keywords: Information Technology, Machine Learning Methods, NIPS 2008, Singular Value Decomposition, Whole-matrix Svd