Stochastic Scale Invariant Power Iteration for KL-divergence Nonnegative Matrix Factorization

We introduce a mini-batch stochastic variance-reduced algorithm to solve finite-sum scale invariant problems which cover several examples in machine learning and statistics such as principal component analysis (PCA) and estimation of mixture proportions. The algorithm is a stochastic generalization...

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Vydané v:	IEEE International Conference on Big Data s. 969 - 977
Hlavní autori:	Kim, Cheolmin, Kim, Youngseok, Jambunath, Yegna Subramanian, Klabjan, Diego
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 15.12.2024
Predmet:	Big Data Convergence Estimation Machine learning Machine learning algorithms Principal component analysis Synthetic data
ISSN:	2573-2978
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