Semi-supervised metric learning via topology preserving multiple semi-supervised assumptions

DOI10.1016/j.patcog.2013.02.015zbMath1323.68447OpenAlexW2065675334MaRDI QIDQ888561

Pong C. Yuen, Qianying Wang, Guo Can Feng

Publication date: 30 October 2015

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2013.02.015

zbMATH Keywords

topology preservingsemi-supervised assumptionssemi-supervised metric learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Learning a metric when clustering data points in the presence of constraints ⋮ Supervised distance metric learning through maximization of the Jeffrey divergence

Uses Software

• UCI-ml

Cites Work

• Distance metric learning by minimal distance maximization
• Learning a Mahalanobis distance metric for data clustering and classification
• Non-linear metric learning using pairwise similarity and dissimilarity constraints and the geometrical structure of data
• Active Reranking for Web Image Search
• Nonlinear Dimensionality Reduction
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item