Algorithm 805
- 1 September 2000
- journal article
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Mathematical Software
- Vol. 26 (3) , 415-435
- https://doi.org/10.1145/358407.358424
Abstract
We present algorithms for computing a semidiscrete approximation to a matrix in a weighted norm, with the Frobenius norm as a special case. The approximation is formed as a weighted sum of outer products of vectors whose elements are ±1 or 0, so the storage required by the approximation is quite small. We also present a related algorithm for approximation of a tensor. Applications of the algorithms are presented to data compression, filtering, and information retrieval; software is provided in C and in Matlab.Keywords
This publication has 3 references indexed in Scilit:
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- Digital Image Compression by Outer Product ExpansionIEEE Transactions on Communications, 1983
- Lower Rank Approximation of Matrices by Least Squares With Any Choice of WeightsTechnometrics, 1979