Abstract: This paper discusses a new class of matrix nearness problems that measure approximation
error using a directed distance measure called a Bregman divergence. Bregman divergences
offer an important generalization of the squared Frobenius norm and relative entropy, and they all
share fundamental geometric properties. In addition, these divergences are intimately connected
with exponential families of probability distributions. Therefore, it is natural to study matrix approximation
problems with respect to Bregman divergences. This article proposes a framework for
studying these problems, discusses some specific matrix nearness problems, and provides algorithms
for solving them numerically. These algorithms apply to many classical and novel problems, and
they admit a striking geometric interpretation.
- Topics:
- Bregman Divergence
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Citation
- Matrix Nearness Problems with Bregman Divergences (pdf, software)
I. Dhillon, J. Tropp.
SIAM Journal of Matrix Analysis and Applications (SIMAX) 29(4), pp. 1120-1146, November 2007.
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