Abstract
We present a novel subspace clustering algorithm SuMC (Subspace Memory Clustering) based on information theory, MDLP (Minimal Description Length Principle) and lossy compression. SuMC simultaneously solves two fundamental problems of subspace clustering: determination of the number of clusters and their optimal dimensions.Although SuMC requires only two parameters: data compression ratio r and a number of bits that are used to code a single scalar, the optimal value of compression ratio can be estimated by the Bayesian information criterion (BIC).We verified that in typical tasks of clustering, image segmentation and data compression, we obtain either better or comparable results to the leading methods of subspace clustering.
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