Non-negative matrix factorization clustering method based on dual local learning

Abstract

The invention provides a non-negative matrix factorization clustering method based on dual local learning. The non-negative matrix factorization clustering method based on dual local learning comprises the steps of S10, selecting a to-be-classified data matrix V, a cluster number a1 and a cluster number a2 according to a to-be-clustered image; S20, constructing an objective function O according tothe data matrix V; S30, outputting a class result by using an iteration method according to the objective function O; and S40, clustering the to-be-clustered images according to the class result. According to the clustering method, double-structure learning is combined, a collaborative clustering problem is converted into a non-negative matrix v problem with orthogonal constraint, the complexityof the problem is simplified, the method has representativeness and universality, the complexity is low, the operation speed in the clustering process is greatly increased, and the clustering efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a non-negative matrix decomposition clustering method based on dual local learning. Background technique [0002] In data mining, machine learning, computer vision and other research fields, the clustering problem is a hard problem, which aims to divide a given set of data objects in a learning task into different clusters, while minimizing the gap within the cluster and maximizing Distinguishability between clusters. Given a data set V, group the data set according to the similarity between data objects, and satisfy: {C i |j=1,2,...,k}, During this process C. i It is called a cluster, and a good clustering method must be able to produce high-quality clustering results—clusters, which must have the following two characteristics: high intra-class similarity and low inter-class similarity. [0003] At present, there are two main types of methods to deal with clusterin...

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Problems solved by technology

[0005] In view of the above problems, the present invention provides a non-negative matrix decomposition clustering method based on dual local learning, which effectively solves the technical problem of excessively high complexity of collaborative clustering in the prior art

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