Hyper-graph regularized discriminative concept factorization for data representation
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DOI: 10.1007/s00500-017-2636-1
Abstract: For the tasks of pattern analysis and recognition, nonnegative matrix factorization and concept factorization (CF) have attracted much attention due to its effective application to find the meaningful low-dimensional representation of data. However, they neglect… read more here.
Keywords: graph regularized; factorization; hyper graph; concept factorization ... See more keywords
Hyper-Graph Partitioning for a Multi-Agent Reformulation of Large-Scale MILPs
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DOI: 10.1109/lcsys.2021.3093338
Abstract: This letter addresses the challenge of solving large-scale Mixed Integer Linear Programs (MILPs). A resolution scheme is proposed for the class of MILPs with a hidden constraint-coupled multi-agent structure. In particular, we focus on the… read more here.
Keywords: graph partitioning; multi agent; hyper graph; reformulation ... See more keywords
Unsupervised Spectral Feature Selection With Dynamic Hyper-Graph Learning
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DOI: 10.1109/tkde.2020.3017250
Abstract: Unsupervised spectral feature selection (USFS) methods could output interpretable and discriminative results by embedding a Laplacian regularizer in the framework of sparse feature selection to keep the local similarity of the training samples. To do… read more here.
Keywords: hyper graph; graph; unsupervised spectral; feature selection ... See more keywords