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Nov, 2023
用于监督矩阵分解的指数收敛算法
Exponentially Convergent Algorithms for Supervised Matrix Factorization
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Joowon Lee, Hanbaek Lyu, Weixin Yao
TL;DR
利用监督矩阵分解(SMF)方法,在高维数据中学习低秩潜在因素,并提供可解释性、数据重构性和类别区分性特征,通过一个新颖框架将SMF提升为一个结合因子空间的低秩矩阵估计问题,并提出了一个有效算法,在各种癌症中成功识别了已知的与癌症相关的基因组。
Abstract
supervised matrix factorization
(SMF) is a classical machine learning method that simultaneously seeks feature extraction and classification tasks, which are not necessarily a priori aligned objectives. Our goal is to use SMF to learn
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