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A Doubly Enhanced EM Algorithm for Model-Based Tensor Clustering

Modern scientific studies often collect datasets in the form of tensors. These datasets call for innovative statistical analysis methods. In particular, there is a pressing need for tensor clustering methods to understand the heterogeneity in the data. We propose a tensor normal mixture model approach to enable probabilistic interpretation and computational tractability. Our statistical model leverages the tensor covariance structure to reduce the number of parameters for parsimonious modeling, and at the same time explicitly exploits the correlations for better variable selection and clustering. We propose a doubly enhanced expectation–maximization (DEEM) algorithm to perform clustering under this model. Both the expectation-step and the maximization-step are carefully tailored for tensor data in order to maximize statistical accuracy and minimize computational costs in high dimensions. Theoretical studies confirm that DEEM achieves consistent clustering even when the dimension of each mode of the tensors grows at an exponential rate of the sample size. Numerical studies demonstrate favorable performance of DEEM in comparison to existing methods.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (JASA); Vol.117 No.540 December 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 2120-2134

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

CLUSTERING
GAUSSIAN MIXTURE MODELS
TENSOR
MINIMAX
THE EM ALGORITHM
KRONECKER PRODUCT COVARIANCE

Info Detail Spesifik

https://doi.org/10.1080/01621459.2021.1904959

Pernyataan Tanggungjawab

Qing Mai, Xin Zhang, Yuqing Pan, Kai Deng

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