Rank-Optimized Logistic Matrix Regression toward Improved Matrix Data Classification

DOI10.1162/neco_a_01038zbMath1471.62429OpenAlexW2768292343WikidataQ47326904 ScholiaQ47326904MaRDI QIDQ5157135

Jianguang Zhang, Jian-Min Jiang

Publication date: 12 October 2021

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco_a_01038

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Generalized linear models (logistic models) (62J12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Cites Work

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• Tensor Decompositions and Applications
• Multiple rank multi-linear SVM for matrix data classification
• Learning kernel logistic regression in the presence of class label noise
• Image representation using Laplacian regularized nonnegative tensor factorization
• Tensor Learning for Regression
• Efficient Image Classification via Multiple Rank Regression
• Ridge Regression: Biased Estimation for Nonorthogonal Problems

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