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Poster
Projection techniques to update the truncated SVD of evolving matrices with applications
Vasileios Kalantzis · Georgios Kollias · Shashanka Ubaru · Athanasios N. Nikolakopoulos · Lior Horesh · Kenneth Clarkson

Thu Jul 22 09:00 PM -- 11:00 PM (PDT) @
in Poster Session 6 »
This submission considers the problem of updating the rank-$k$ truncated Singular Value Decomposition (SVD) of matrices subject to the addition of new rows and/or columns over time. Such matrix problems represent an important computational kernel in applications such as Latent Semantic Indexing and Recommender Systems. Nonetheless, the proposed framework is purely algebraic and targets general updating problems. The algorithm presented in this paper undertakes a projection viewpoint and focuses on building a pair of subspaces which approximate the linear span of the sought singular vectors of the updated matrix. We discuss and analyze two different choices to form the projection subspaces. Results on matrices from real applications suggest that the proposed algorithm can lead to higher accuracy, especially for the singular triplets associated with the largest modulus singular values. Several practical details and key differences with other approaches are also discussed.
Keywords: Dimensionality Reduction · Algorithms

Author Information

Vasileios Kalantzis (IBM Research)

https://www-users.cs.umn.edu/~kalan019/

Georgios Kollias (IBM)
Shashanka Ubaru (IBM Research)
Athanasios N. Nikolakopoulos (Amazon)
Lior Horesh (IBM Research)
Kenneth Clarkson (IBM Research)

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