Multiresolution matrix factorization

Risi Kondor, Nedelina Teneva, Vikas K. Garg

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

17 Citations (Scopus)


Large matrices appearing in machine learning problems often have complex hierarchical structures that go beyond what can be found by traditional linear algebra tools, such as eigende-composition. Inspired by ideas from multiresolution analysis, this paper introduces a new notion of matrix factorization that can capture structure in matrices at multiple different scales. The resulting Multiresolution Matrix Factorizations (MMFs) not only provide a wavelet basis for sparse approximation, but can also be used for matrix compression (similar to Nyström approximations) and as a prior for matrix completion.

Original languageEnglish
Title of host publication31st International Conference on Machine Learning, ICML 2014
Number of pages11
ISBN (Electronic)9781634393973
Publication statusPublished - 2014
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Machine Learning - Beijing, China
Duration: 21 Jun 201426 Jun 2014
Conference number: 31


ConferenceInternational Conference on Machine Learning
Abbreviated titleICML


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