Leveraging maximum entropy and correlation on latent factors for learning representations
type: Item
instance of: scholarly article
title: Leveraging maximum entropy and correlation on latent factors for learning representations
zbMATH Open document ID: 1475.68270
author: Z. C. He
author: Jie Liu
author: Kai Dang
author: Fuzhen Zhuang
author: Yalou Huang
published in: Neural Networks
publication date: 2021-12-07T00:00:00Z
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 15A23
Mathematics Subject Classification ID: 62H25
Mathematics Subject Classification ID: 94A17
zbMATH DE Number: 7439676
zbMATH Keywords: non-negative matrix factorization
zbMATH Keywords: maximum entropy
zbMATH Keywords: correlated latent factor learning
Wikidata QID: Q99201041
describes a project that uses: NUS-WIDE
describes a project that uses: GeoMF
MaRDI profile type: Publication
full work available at URL: j.neunet.2020.07.027
OpenAlex ID: W3047349556
cites work: 10.1162/jmlr.2003.3.4-5.993
cites work: Q3093232
cites work: Principal component analysis.
cites work: A column-wise update algorithm for nonnegative matrix factorization in Bregman divergence with an orthogonal constraint
cites work: Learning the parts of objects by non-negative matrix factorization
cites work: Weakly Supervised Deep Matrix Factorization for Social Image Understanding
cites work: Projected Gradient Methods for Nonnegative Matrix Factorization
cites work: A survey of multilinear subspace learning for tensor data
cites work: Rank Selection in Nonnegative Matrix Factorization using Minimum Description Length
DBLP publication ID: journals/nn/He0DZH20
DOI: 10.1016/J.NEUNET.2020.07.027
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description: scientific article; zbMATH DE number 7439676