Proceedings ArticleDOI
Evolutionary Distance Metric Learning Approach to Semi-supervised Clustering with Neighbor Relations
Ken-ichi Fukui,Satoshi Ono,Taishi Megano,Masayuki Numao +3 more
- pp 398-403
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TLDR
This study proposes a distance metric learning method based on a clustering index with neighbor relation that simultaneously evaluates inter-and intra-clusters and optimizes a distance transform matrix based on the Mahalanobis distance by utilizing a self-adaptive differential evolution algorithm.Abstract:
This study proposes a distance metric learning method based on a clustering index with neighbor relation that simultaneously evaluates inter-and intra-clusters. Our proposed method optimizes a distance transform matrix based on the Mahalanobis distance by utilizing a self-adaptive differential evolution (jDE) algorithm. Our approach directly improves various clustering indices and in principle requires less auxiliary information compared to conventional metric learning methods. We experimentally validated the search efficiency of jDE and the generalization performance.read more
Citations
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Evolutionary multi-objective distance metric learning for multi-label clustering
TL;DR: Experimental results have shown that the proposed DML method produces better transform matrices than single-objective optimization and is helpful in finding the attributes that affect the trade-off relationship among objective functions.
References
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Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
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Proceedings Article
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
Mikhail Belkin,Partha Niyogi +1 more
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Proceedings Article
Distance Metric Learning for Large Margin Nearest Neighbor Classification
TL;DR: In this article, a Mahanalobis distance metric for k-NN classification is trained with the goal that the k-nearest neighbors always belong to the same class while examples from different classes are separated by a large margin.
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