Multi-linear large space feature extraction method

Abstract

The invention provides a multi-linear large space feature extraction method. The method comprises the following steps: firstly, extracting gait sequence data of one period of a video stream sample, and expressing the gait sequence data as a tensor structure; secondly, preprocessing tensor data through multi-linear principal component analysis (MPCA), projecting the high-dimensional tensor data to a low-dimensional tensor structure, and removing redundancy and noise information; thirdly, optimizing the difference between inter-class Laplacian divergence and intra-class Laplacian divergence, maximizing the value of the difference, continuously performing iteration evaluation to remove convergence and achieve an iteration end condition so as to obtain projection matrixes under various modes, and obtaining a low-dimensional tensor with lower dimensionality and supervisory information through tensorial multiplication; and finally, classifying through a nearest neighbor classifier based on Euclidean distance. The method provided by the invention has a higher recognition rate compared with the MPCA, and the final feature with reduced dimensionality is shorter.

Description

technical field [0001] The invention belongs to the field of machine learning and pattern recognition, in particular to a multi-linear and large-spacing feature extraction method. Background technique [0002] With the improvement of data collection and storage capacity, a large amount of multi-dimensional data is generated every day in various application fields, and effective feature extraction methods become particularly important. Traditional vector-based dimensionality reduction methods, such as principal component analysis (Principal component analysis, PCA), linear discriminant analysis (Linear discriminant analysis, LDA) and local preservation projection (Local preserve projection, LPP) are to transform the image matrix into a more High-dimensional vectors will inevitably bring about the curse of dimensionality in image sequence recognition. Moreover, methods using Fisher's discriminant criterion (such as LDA) need to calculate the inverse of the intra-class scatter...

AI Technical Summary

Problems solved by technology

[0010] The traditional vector-based feature extraction method is realized by vectorizing the image matrix (or image sequence), but it will cause dimensionality disaster and destroy the structural information of the original features in multi-modal data dimension reduction

Although the existing tensor methods, MPCA and GTDA have achieved encouraging recognition results, there is still room for improvement in recognition performance.

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