Semi-supervised clustering with discriminative random fields

DOI10.1016/j.patcog.2012.05.021zbMath1248.68399OpenAlexW2043829186MaRDI QIDQ454430

Chin-Chun Chang, Hsin-Yi Chen

Publication date: 5 October 2012

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2012.05.021

zbMATH Keywords

discriminative random fieldssemi-supervised clustering

Mathematics Subject Classification ID

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

Related Items (3)

A pattern recognition framework for detecting dynamic changes on cyclic time series ⋮ Active learning for semi-supervised clustering based on locally linear propagation reconstruction ⋮ Weight-based label-unknown multi-view data set generation approach

Uses Software

• SeDuMi
• UCI-ml
• BoostCluster
• ElemStatLearn

Cites Work

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• Semi-supervised clustering with metric learning: an adaptive kernel method
• Non-linear metric learning using pairwise similarity and dissimilarity constraints and the geometrical structure of data
• Kernel-based metric learning for semi-supervised clustering
• Semi-supervised graph clustering: a kernel approach
• Improving fuzzy clustering of biological data by metric learning with side information
• Robust path-based spectral clustering
• Active semi-supervised fuzzy clustering
• Locally linear metric adaptation with application to semi-supervised clustering and image retrieval
• Relaxational metric adaptation and its application to semi-supervised clustering and content-based image retrieval
• Enhancement of fuzzy clustering by mechanisms of partial supervision
• Markov Random Field Modeling in Image Analysis
• The Consistent Labeling Problem: Part I
• Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric cones
• Semidefinite Programming
• Computer Vision - ECCV 2004
• The elements of statistical learning. Data mining, inference, and prediction
• A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters

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