Zero-sample classification method based on low-rank representation and manifold regularization

Abstract

The invention discloses a zero-sample classification method based on low-rank representation and manifold regularization. The method comprises the steps of calculating a mapping relation between visual features and semantic features of samples in a visible data set; calculating semantic representations of samples in an invisible data set; introducing sparse constraints and in combination with Laplacian regularization constraints, calculating low-rank representations of the samples in the invisible data set; calculating a weight matrix and a Laplacian matrix; introducing the manifold regularization, and removing noises of the semantic representations in the invisible data set; and predicting labels of the samples in the invisible data set, thereby realizing sample classification. Accordingto the zero-sample classification method based on the low-rank expression and the manifold regularization, the classification method effectively overcomes the limitation of low classification precision under the conditions of few samples, sample label information loss and the like in a traditional classification method; the more accurate semantic representations in the invisible data set are obtained; the description capability of data features is enhanced; and the precision of zero-sample classification can be effectively improved.

Description

technical field [0001] The invention relates to the technical field of sample classification, in particular to a zero-sample classification method based on low-rank representation and manifold regularization. Background technique [0002] In large-scale classification problems, the lack of enough training samples, or the loss of label information of many samples, limits the improvement of classification accuracy to some extent. Zero-shot classification is an effective solution to this problem. [0003] In the prior art, it is generally assumed that sample data are distributed in low-dimensional subspaces and have a low-rank structure. Existing methods focus on finding low-rank representations of data based on the assumption that the data distribution approximately spans multiple low-dimensional subspaces. it passes l 1 / l 2 The norm deals with outliers, and under certain technical conditions, the subspace structure of the sample is accurately restored, and the outliers a...

AI Technical Summary

Problems solved by technology

[0005] The invention provides a zero-sample classification method based on low-rank representation and manifold regularization, which solves the current technical problem of low classification accuracy when there are insufficient training samples or the label information of the samples is lost

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