Deep learning-based clustering method

Abstract

The invention discloses a deep learning-based clustering method. The method comprises the following steps: obtaining the initial network weight of a deep neural network; grouping samples randomly and mapping to a feature space; adding the target function of the original deep neural network into the in-class constraint function of a feature layer; updating the network weight of the deep neural network and calculating to obtain a new feature layer; distributing all the samples to the class group of the nearest clustering center and calculating a new clustering center; substituting the new clustering center for the clustering center of the in-class constraint function, and returning to the network weight updating step to perform iteration to obtain and output the final clustering classification result. According to the method, the samples are subjected to non-linear mapping of the deep neural network from the original data space, which is difficult to cluster, to obtain a height-classified feature for clustering; a better clustering effect can be achieved by continuously optimizing the network structure. The deep learning-based clustering method with lower memory consumption and higher clustering precision is superior to the conventional clustering algorithm.

Description

technical field [0001] The invention relates to the technical fields of pattern recognition and machine learning, in particular to a clustering method based on deep learning. Background technique [0002] At present, traditional clustering algorithms are only suitable for situations where the data space is linearly separable, such as the K-means algorithm. However, with the increase of the amount of data and the increase of data complexity, the traditional clustering algorithm has been unable to perform the clustering task under the background of complex large-scale data. Although some clustering algorithms have recently been proposed to solve this problem, such as spectral clustering and other algorithms, such algorithms cannot be used in the background environment of big data because they require huge memory consumption. [0003] Therefore, in view of the fact that the previous methods are difficult to meet the current needs of large-scale complex data clustering, the pre...

AI Technical Summary

Problems solved by technology

However, with the increase in the amount of data and the increase in data complexity, traditional clustering algorithms are no longer competent for clustering tasks in the context of complex large-scale data.

Although some clustering algorithms have recently been proposed to solve this problem, such as spectral clustering and other algorithms, but due to the huge memory consumption of such algorithms, they are not suitable for the background environment of big data.

[0003] Ther

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