Subspace Clustering through United Entropy Matrix
Niu Ku · Beijing Youdian Xueyuan xuebao · 2014
Recent subspace clustering research results suffer from two problems: firstly,they typically scale exponentially with the data dimensionality or the subspace dimensionality of clusters. Secondly,present methods are often sensitive to input parameters. To overcome these limitations,a subspace clustering algorithm based on united entropy matrix( UEM) is presented. In the method,entropy is used to filter out redundant attributes and UEM is used to store united entropy of each two attributes. This method finds all interesting subspaces in UEM by searching all-one sub matrix. Finally,all subspace clusters can be gotten by clustering on interesting subspaces. The evaluation on both synthesis and real datasets show that our approach outperforms traditional subspace clustering methods and provides enhanced quality for finding subspace clusters with higher dimensions.