A0399
Title: Model-based low-rank tensor clustering
Authors: Junge Li - Florida State University (United States)
Qing Mai - Florida State University (United States) [presenting]
Abstract: Tensors have become prevalent in business applications and scientific studies. Analyzing and understanding the heterogeneity in tensor-variate observations is of great interest. A novel tensor low-rank mixture model (TLMM) IS proposed to conduct efficient estimation and clustering on tensors. The model combines the Tucker low-rank structure in mean contrasts and the separable covariance structure to achieve parsimonious and interpretable modelling. A low-rank enhanced expectation-maximization (LEEM) algorithm is developed to implement efficient computation under this model. The pseudo-E-step and the pseudo-M-step are carefully designed to incorporate variable selection and efficient parameter estimation. Numerical results in extensive experiments demonstrate the encouraging performance of the proposed method compared to popular vector and tensor methods.
