Image dimension reduction clustering method based on fuzzy theory

Abstract

The invention discloses an image dimension reduction clustering method based on a fuzzy theory, which comprises the following steps of: firstly initializing a projection matrix U, a membership matrix Y, a clustering center matrix M, a projected sample matrix V and a regularization parameter lambda, then alternately updating V, M and Y by adopting an alternate optimization algorithm, and repeatedly iterating until an objective function is converged to realize unsupervised data dimension reduction. According to the invention, the unsupervised method, namely the fuzzy principal component dimension reduction and clustering method (FPCPC), which can be used for carrying out dimension reduction and clustering at the same time, is realized. According to the method, the dimensionality reduction of the image data and the clustering in the subspace are simultaneously realized in one method, the efficiency is improved, and the loss of category information in the dimensionality reduction process of the image is reduced.

Description

technical field [0001] The invention belongs to the technical field of machine learning, and in particular relates to an image dimension reduction clustering method. Background technique [0002] Dimensionality reduction and clustering are the two most popular algorithms in the field of machine learning. By projecting high-dimensional data into a low-dimensional space, dimensionality reduction eliminates redundant information and noise information in the original data, and retains the most important data features. It alleviates the problem of the "curse of dimensionality" brought about by high dimensions. In production practice, due to hardware failures, programming errors, program identification errors, etc., the obtained data often contains redundancy and noise. These redundancy and noise will not only cause the subsequent data processing operations to be complicated and slow, but also make the data processing more difficult. The result deviates from the true direction. ...

AI Technical Summary

Problems solved by technology

The methods in these production practices are to reduce the dimensionality and cluster the data step by step. The algorithm is divided into two steps to reduce the efficiency of the algorithm, and the category information may be lost in the process of dimensionality reduction, reducing the clustering accuracy, so it can be considered Combine dimensionality reduction and clustering into one method to improve algorithm efficiency and reduce loss of image category information

Images