Human Behavior Recognition Based on Sparse Tensor Discriminant Analysis

doi:10.3969/j.issn.1006-2475.2020.03.023

Abstract

Abstract: In pattern recognition, how to reduce the dimension and identify the samples while extracting the key features is one of research hotspots. Based on local Fisher discriminant analysis (LFDA), this paper proposes a feature extraction method combining tensor representation with sparse analysis: Sparse Tensor Local Fishers Discriminant Analysis (STLFDA). This method transforms the feature decomposition problem in tensor local Fisher discriminant analysis (TLFDA) algorithm into linear regression problem, and solves the feature selection problem in linear regression with elastic network. It not only satisfies the goal of the Tensor Local Fisher Discriminant Analysis, but also guarantees the sparsity of the projection matrix. The validity of STLFDA algorithm is demonstrated by experiments on the Weizmann human behavior database.

Key words: local Fisher discriminant analysis, sparse analysis, tensor representation, elastic network

CLC Number:

TP391.4

LU Yu-tong, HAN Li-xin. Human Behavior Recognition Based on Sparse Tensor Discriminant Analysis[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2020.03.023.

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