Deep migration embedded clustering machine learning method based on local structure preservation

Abstract

The invention relates to a deep migration embedded clustering machine learning method based on local structure preservation, The method is based on the local structure of the data generation distribution saved by the incomplete automatic encoder, fuses the clustering loss and the reconstruction loss to establish a clustering optimization model, and solves the clustering optimization model througha small batch of stochastic gradient descent and back propagation algorithm to achieve clustering. Compared with the prior art, the present invention solves the problem that the prior DEC method cannot save the local structure of data, and has the advantages of simple method and high clustering accuracy.

Description

technical field [0001] The invention belongs to the technical field of computer vision and pattern recognition, and relates to a deep migration embedding clustering method, in particular to a deep migration embedding clustering machine learning method based on local structure preservation. Background technique [0002] Unsupervised clustering is an important research topic in data science and machine learning. Traditional clustering algorithms such as k-means, Gaussian mixture models, and spectral clustering group data on hand-designed features based on intrinsic properties or similarities. However, when the dimensionality of the input feature space (data space) is high, the similarity measure will become unreliable, so that effective clustering results cannot be obtained. Mapping data from high-dimensional feature space to low-dimensional space and then clustering is an intuitive solution and has been widely studied. This can be achieved by applying dimensionality reducti...

AI Technical Summary

Problems solved by technology

However, this clustering loss cannot guarantee that the learned features can preserve the local structure of the original data

Therefore, feature transformations can be misguided, leading to the construction of collapsed embedding feature spaces, i.e. learning invalid data representations

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