Conscience online learning: an efficient approach for robust kernel-based clustering |
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Authors: | Chang-Dong Wang Jian-Huang Lai Jun-Yong Zhu |
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Affiliation: | (1) Department of Psychological Medicine, School of Medicine, University Hospital Wales, Heath Park, Cardiff, Wales, UK, CF14 4XN |
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Abstract: | Kernel-based clustering is one of the most popular methods for partitioning nonlinearly separable datasets. However, exhaustive
search for the global optimum is NP-hard. Iterative procedure such as k-means can be used to seek one of the local minima. Unfortunately, it is easily trapped into degenerate local minima when
the prototypes of clusters are ill-initialized. In this paper, we restate the optimization problem of kernel-based clustering
in an online learning framework, whereby a conscience mechanism is easily integrated to tackle the ill-initialization problem and faster convergence rate is achieved. Thus, we
propose a novel approach termed conscience online learning (COLL). For each randomly taken data point, our method selects the winning prototype based on the conscience mechanism to bias the ill-initialized prototype to avoid degenerate local minima and efficiently updates the winner by the
online learning rule. Therefore, it can more efficiently obtain smaller distortion error than k-means with the same initialization. The rationale of the proposed COLL method is experimentally analyzed. Then, we apply
the COLL method to the applications of digit clustering and video clustering. The experimental results demonstrate the significant
improvement over existing kernel-based clustering methods. |
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