Low-rank projection feature extraction method under label missing condition

Abstract

The invention discloses a low-rank projection feature extraction method under a label missing condition, and the method comprises the steps: carrying out the detection of sample data and labels one byone, and obtaining a current sample and a corresponding label; and if the current sample label belongs to the normal range, continuing to detect the next sample, and if the current sample label is missing, calculating the Euclidean distance between each sample and the sample in the original space, solving the label with the maximum possibility through a k-nearest neighbor principle, and writing the label corresponding to the sample into the original data set. After label compensation, a nearest neighbor graph matrix is constructed, a projection matrix is learned through a PCA algorithm, the projection matrix is applied to the test set, and classification is performed by using a classifier. The method provided by the invention can adaptively provide more accurate and reasonable training data for various classification models so as to help a classifier to generate a better classification model and can improve the accuracy and robustness of image classification.

Description

technical field [0001] The invention relates to a method for extracting low-rank projection features in the case of missing labels, in particular to a method for ensuring low-rank projection feature extraction by restoring labels, and belongs to the field of machine vision and pattern recognition. Background technique [0002] Image classification often requires dimensionality reduction and feature extraction of high-dimensional data to meet the needs of classifiers, and dimensionality reduction will cause information loss and energy reduction. A typical Principal Component Analysis (PCA) algorithm is a The unsupervised classification projection learning algorithm aims to learn an optimal low-rank projection matrix from the training data, and apply the learned projection matrix to the training set and test set, which can simultaneously reduce the dimensionality of the training set and the test set. The purpose of extracting features. The projection matrix obtained by using ...

AI Technical Summary

Problems solved by technology

When the PCA algorithm learns the projection matrix, the training set is randomly selected. If the samples selected in the training set have missing labels, it will affect the effect of the learned projection matrix, and then affect the accuracy of the classification.

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