Marginal semi-supervised sub-manifold projections with informative constraints for dimensionality reduction and recognition

DOI10.1016/j.neunet.2012.09.010zbMath1258.68132OpenAlexW2058514664WikidataQ43662751 ScholiaQ43662751MaRDI QIDQ1942726

Tommy W. S. Chow, Zhao Zhang, Mingbo Zhao

Publication date: 13 March 2013

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

Full work available at URL: https://doi.org/10.1016/j.neunet.2012.09.010

zbMATH Keywords

image recognitiondimensionality reductionsemi-supervised learninginformative constraintsmarginal projections

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (9)

Adaptive non-negative projective semi-supervised learning for inductive classification ⋮ Learning a metric when clustering data points in the presence of constraints ⋮ Marginal semi-supervised sub-manifold projections with informative constraints for dimensionality reduction and recognition ⋮ Learning from normalized local and global discriminative information for semi-supervised regression and dimensionality reduction ⋮ Unnamed Item ⋮ Semi-supervised double sparse graphs based discriminant analysis for dimensionality reduction ⋮ Discriminative sparse flexible manifold embedding with novel graph for robust visual representation and label propagation ⋮ Similarity preserving low-rank representation for enhanced data representation and effective subspace learning ⋮ A general soft label based linear discriminant analysis for semi-supervised dimensionality reduction

Uses Software

• darch

Cites Work

• Unnamed Item
• Unnamed Item
• Constrained large margin local projection algorithms and extensions for multimodal dimensionality reduction
• LPP solution schemes for use with face recognition
• Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction
• A unified framework for semi-supervised dimensionality reduction
• Learning a Mahalanobis distance metric for data clustering and classification
• Bagging constraint score for feature selection with pairwise constraints
• Non-linear metric learning using pairwise similarity and dissimilarity constraints and the geometrical structure of data
• Semi-supervised learning based on high density region estimation
• Laplacian twin support vector machine for semi-supervised classification
• Marginal semi-supervised sub-manifold projections with informative constraints for dimensionality reduction and recognition
• Semi-supervised local Fisher discriminant analysis for dimensionality reduction
• Constraint score: A new filter method for feature selection with pairwise constraints
• Local multidimensional scaling
• LAPACK95 Users' Guide
• Trace optimization and eigenproblems in dimension reduction methods
• Reducing the Dimensionality of Data with Neural Networks
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation

This page was built for publication: Marginal semi-supervised sub-manifold projections with informative constraints for dimensionality reduction and recognition