Later fusion multi-kernel clustering machine learning method and system based on proxy graph improvement

Abstract

The invention discloses a later fusion multi-kernel clustering machine learning method and system based on proxy graph improvement. The later fusion multi-kernel clustering machine learning method based on proxy graph improvement involved in the invention comprises the following steps: S1, acquiring a clustering task and a target data sample; S2, initializing a proxy graph improvement matrix; S3, performing k-means clustering and graph improvement on each view corresponding to the obtained clustering task and the target data sample, and constructing a target function in combination with a kernel k-means clustering and graph improvement method; S4, solving the objective function constructed in the step S3 by adopting a circulation mode to obtain a graph matrix fused with basic kernel information; and S5, performing spectral clustering on the obtained graph matrix to obtain a final clustering result. The optimized basic division not only has information of a single core, but also can obtain global information through the proxy graph, and fusion of views is more facilitated, so that the learned proxy graph can better fuse information of each core matrix, and the purpose of improving the clustering effect is achieved.

Description

technical field [0001] The present invention relates to the technical field of machine learning, in particular to a later-stage fusion multi-core clustering machine learning method and system based on agent graph improvement. Background technique [0002] Clustering plays an important role in machine learning and data analysis, and its goal is to divide unlabeled data into several unrelated classes. In the era of big data, data is collected from multiple sources, and this type of data is called multi-view data. Methods for clustering multi-view data are known as multi-view clustering algorithms. Multi-kernel clustering algorithm is an important branch of multi-view clustering, which aims to make full use of a series of predefined base kernels to improve clustering performance. [0003] The existing multi-kernel clustering algorithms can be roughly divided into two types: early fusion and late fusion according to the timing of fusion. Early fusion refers to the fusion of s...

AI Technical Summary

Problems solved by technology

However, the existing post-fusion clustering algorithms still have the following shortcomings: First, the clustering process of the basic kernel and the post-fusion process of the basic partition are separated

In this case, the quality of the basic division has a great influence on the performance of the final clustering. If there are abnormal points and noise, the clustering effect will be unsatisfactory.

The second is that the existing methods simply regard the consistent partition as a linear transformation of the basic partition, making it difficult to apply to multi-core data in reality

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