Fast and Accurate Least-Mean-Squares Solvers for High Dimensional Data
| Autor: | Alaa Maalouf, Ibrahim Jubran, Dan Feldman |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | IEEE Transactions on Pattern Analysis and Machine Intelligence. 44:9977-9994 |
| ISSN: | 1939-3539 0162-8828 |
| DOI: | 10.1109/tpami.2021.3139612 |
| Popis: | Least-mean-squares (LMS) solvers such as Linear / Ridge-Regression and SVD not only solve fundamental machine learning problems, but are also the building blocks in a variety of other methods, such as matrix factorizations. We suggest an algorithm that gets a finite set of n d-dimensional real vectors and returns a subset of d+1 vectors with positive weights whose weighted sum is exactly the same. The constructive proof in Caratheodory's Theorem computes such a subset in O(n |
| Databáze: | OpenAIRE |
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