Sciweavers

Share
NIPS
2008

QUIC-SVD: Fast SVD Using Cosine Trees

11 years 1 months ago
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.
Michael P. Holmes, Alexander G. Gray, Charles Lee
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Michael P. Holmes, Alexander G. Gray, Charles Lee Isbell Jr.
Comments (0)
books