Efficient Algorithms for Constructing an Interpolative Decomposition

Publication date: 14 May 2021

Abstract: Low-rank approximations are essential in modern data science. The interpolative decomposition provides one such approximation. Its distinguishing feature is that it reuses columns from the original matrix. This enables it to preserve matrix properties such as sparsity and non-negativity. It also helps save space in memory. In this work, we introduce two optimized algorithms to construct an interpolative decomposition along with numerical evidence that they outperform the current state of the art.

Has companion code repository: https://github.com/rishi1999/random-projections

Mathematics Subject Classification ID

Numerical methods for low-rank matrix approximation; matrix compression (65F55)

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