Sparse subspace clustering algorithm based on semi-supervision

Abstract

The invention discloses a sparse subspace clustering algorithm based on semi-supervision. The sparse subspace clustering algorithm comprises the steps that data prior information is converted into a constraint matrix suitable for a sparse subspace model in the form of point pair constraint; interference of flag-free bits is eliminated in the form of Hadamard product, the state of the coefficient represented by different constraint conditions is also considered and corresponding constraint terms are established; and a semi-supervised sparse subspace model of two hard threshold and soft threshold forms is established by using the constraint terms, and a semi-supervised framework is accordingly established on the sparse subspace clustering algorithm. The clustering accuracy of the sparse subspace algorithm can still be maintained by the algorithm without prior information. Meanwhile, the performance advantages of the sparse subspace clustering algorithm are also absorbed so that the high-dimensional clustering problem containing interference information data can be directly and effectively processed, the clustering performance is ensured to be effectively enhanced under the condition of less known prior information and thus the algorithm applicability can be increased.

Description

technical field [0001] The invention belongs to the algorithm for data classification in the field of data mining, specifically a sparse subspace clustering algorithm based on semi-supervised learning Background technique [0002] Clustering is an unsupervised learning method. Its purpose is to gather the same kind of data together as much as possible and separate the different kinds of data as much as possible, so as to reveal the inherent nature and laws of the data and provide a basis for further data analysis. Sparse subspace clustering algorithm is a relatively advanced clustering algorithm at present, which can effectively deal with high-dimensional and noisy data. Different from other types of clustering algorithms, sparse subspace clustering uses data self-representation characteristics and data sparsity in a specific space to describe data, through a self-representation model with complete theoretical proof, that is, sparse subspace model, Obtain the sparse represe...

AI Technical Summary

Problems solved by technology

However, as a sparse subspace clustering that is different from other clustering algorithms, the existing semi-supervised framework cannot be directly introduced into the sparse subspace model to guide data classification.

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