A Weight-Adaptive Laplacian Embedding for Graph-Based Clustering.
Neural Computation(2017)
摘要
Graph-based clustering methods perform clustering on a fixed input data graph. Thus such clustering results are sensitive to the particular graph construction. If this initial construction is of low quality, the resulting clustering may also be of low quality. We address this drawback by allowing the data graph itself to be adaptively adjusted in the clustering procedure. In particular, our propos...
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