DC Field | Value | Language |
---|---|---|
dc.contributor.author | Han, Insu | ko |
dc.contributor.author | Avron, Haim | ko |
dc.contributor.author | Shin, Jinwoo | ko |
dc.date.accessioned | 2020-12-15T06:30:30Z | - |
dc.date.available | 2020-12-15T06:30:30Z | - |
dc.date.created | 2020-12-02 | - |
dc.date.created | 2020-12-02 | - |
dc.date.issued | 2020-07-15 | - |
dc.identifier.citation | Thirty-seventh International Conference on Machine Learning, ICML 2020, pp.3942 - 3951 | - |
dc.identifier.issn | 2640-3498 | - |
dc.identifier.uri | http://hdl.handle.net/10203/278494 | - |
dc.description.abstract | This paper studies how to sketch element-wise functions of low-rank matrices. Formally, given low-rank matrix A = [Aij] and scalar non-linear function f, we aim for finding an approximated low-rank representation of the (possibly high-rank) matrix [f(Aij)]. To this end, we propose an efficient sketching-based algorithm whose complexity is significantly lower than the number of entries of A, i.e., it runs without accessing all entries of [f(Aij)] explicitly. The main idea underlying our method is to combine a polynomial approximation of f with the existing tensor sketch scheme for approximating monomials of entries of A. To balance the errors of the two approximation components in an optimal manner, we propose a novel regression formula to find polynomial coefficients given A and f. In particular, we utilize a coreset-based regression with a rigorous approximation guarantee. Finally, we demonstrate the applicability and superiority of the proposed scheme under various machine learning tasks. | - |
dc.language | English | - |
dc.publisher | International Conference on Machine Learning | - |
dc.title | Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix | - |
dc.type | Conference | - |
dc.identifier.wosid | 000683178504010 | - |
dc.identifier.scopusid | 2-s2.0-85105257607 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 3942 | - |
dc.citation.endingpage | 3951 | - |
dc.citation.publicationname | Thirty-seventh International Conference on Machine Learning, ICML 2020 | - |
dc.identifier.conferencecountry | AU | - |
dc.identifier.conferencelocation | Virtual | - |
dc.contributor.localauthor | Shin, Jinwoo | - |
dc.contributor.nonIdAuthor | Avron, Haim | - |
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