Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning
type: Item
instance of: scholarly article
title: Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning
zbMATH Open document ID: 1394.68307
author: Yong Peng
author: Bao-Liang Lu
author: Suhang Wang
published in: Neural Networks
publication date: 2018-08-30T00:00:00Z
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68T10
zbMATH DE Number: 6929186
zbMATH Keywords: low-rank representation
zbMATH Keywords: sparse manifold adaption
zbMATH Keywords: graph construction
zbMATH Keywords: semi-supervised learning
zbMATH Keywords: face recognition
describes a project that uses: l1_ls
MaRDI profile type: Publication
full work available at URL: j.neunet.2015.01.001
OpenAlex ID: W2104144964
Wikidata QID: Q41507785
cites work: A Singular Value Thresholding Algorithm for Matrix Completion
cites work: Robust principal component analysis?
cites work: Learning with $\ell ^{1}$-graph for image analysis
cites work: Active subspace: toward scalable low-rank learning
cites work: A cellular automaton model based on empirical observations of a driver's oscillation behavior reproducing the findings from Kerner's three-phase traffic theory
cites work: Semi-supervised orthogonal discriminant analysis via label propagation
cites work: Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
cites work: Q4864293
cites work: Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization
cites work: Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
cites work: Graph Regularized Sparse Coding for Image Representation
DOI: 10.1016/J.NEUNET.2015.01.001
description: scientific article; zbMATH DE number 6929186