Semi-supervised image clustering subspace learning algorithm based on local linear regression

Abstract

The invention discloses a semi-supervised image clustering subspace learning algorithm based on local linear regression. Firstly, a local linear regression model is used for predicting a coordinate of a training sample in a clustering subspace, a local prediction error between a predicted value and a true value is obtained, and then a minimized objective function of a total predicted error is obtained; then according to two constrain conditions of inter-class dispersion maximization and inner-class dispersion minimization, and a marked sample and an unmarked sample are used for calculating an inter-class dispersion matrix and a total dispersion matrix; and finally, the inter-class dispersion matrix and the total dispersion matrix are blended in the minimized objective function of the total predicted error to obtain an objective function for solving clustering subspace, and function solving is performed through generalized characteristic root to obtain the optimal clustering subspace. The semi-supervised image clustering subspace learning algorithm based on the local linear regression makes full use of the marked sample, the unmarked sample and a local adjacent relation in a training data set to obtain good clustering results.

Description

technical field [0001] The invention belongs to the technical field of semi-supervised image clustering subspace learning algorithms. In particular, it concerns a semi-supervised image clustering subspace learning algorithm based on local linear regression. Background technique [0002] With the popularity of digital cameras and the development of information technology, more and more large-scale image databases have emerged, and a considerable proportion of images are unlabeled and unclassified. A picture is worth a thousand words, and the semantic information expressed by image data is relatively rich. Therefore, manual labeling and classification are not only time-consuming, laborious and costly, but also difficult to achieve unified standards and objective results. This makes the efficient management and effective use of the image database very important and difficult. [0003] Image clustering algorithms can mine potential high-level semantic relationships from underl...

AI Technical Summary

Problems solved by technology

[0007] However, unsupervised learning completely takes unlabeled samples as the learning object, and it is difficult to compare with supervised learning in terms of accuracy. However, in order to achieve ideal results, supervised learning requires sufficient labeled samples.

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