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Sparsity subspace clustering method for distributed implementation

A clustering method and subspace technology, applied in character and pattern recognition, instruments, computer components, etc., can solve problems such as slow convergence speed, poor judgment of stopping criteria, unfavorable distributed implementation, etc., and achieve the goal of reducing processing time Effect

Inactive Publication Date: 2017-06-13
NANJING UNIV OF INFORMATION SCI & TECH
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Problems solved by technology

However, ADMM requires more iterations, slow convergence speed, difficult to judge the stopping criterion, and mutual coupling between reference quantities, so it is not conducive to distributed implementation

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  • Sparsity subspace clustering method for distributed implementation
  • Sparsity subspace clustering method for distributed implementation
  • Sparsity subspace clustering method for distributed implementation

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Embodiment Construction

[0019] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0020] join figure 1 with figure 2 , a sparse subspace clustering method for distributed implementation provided by the present invention, it first replaces the commonly used ADMM algorithm with the coordinate descent method in the process of solving the Lasso problem of the similarity matrix, and then uses the coordinate descent method to solve the Lasso problem The separability of the problem process makes the problem distributed computing.

[0021] The method of the present invention comprises the following steps: on a cluster composed of multiple computers, the data is distributed to each computing node, and then each computing node selects the data of this computer and other computers to...

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Abstract

The invention discloses a sparsity subspace clustering method for distributed implementation. The method comprises the steps of distributing data to each computing node on a cluster consisting of a plurality of computers; then selecting data of a current computer and other computers by each computing node to compute a Lasso sparse reconstruction sub-problem until the problem is converged; after all sub-problems are computed in a labor division manner by all computing nodes, summarizing computing result vectors to a main process or a management node, and performing subsequent weighted undirected graph generation and spectral clustering processes; and finally obtaining classification numbers. For relatively common ADMM serial computation, the computing speed is remarkably increased without reducing the classification accuracy.

Description

technical field [0001] The invention discloses a sparse subspace clustering method implemented in a distributed manner, and relates to the technical field of machine learning data processing. Background technique [0002] Clustering is one of the important issues in unsupervised machine learning research, and it has been widely studied and applied in image processing, data mining, social networks and other fields. However, in many practical problems, with the continuous and rapid increase of data dimensions, the so-called "curse of dimensionality" problem is becoming more and more prominent. If high-dimensional data is solved by traditional methods, the time complexity is often unbearable. Therefore, efficient modeling and calculation of high-dimensional data has become an important challenge and difficulty in data mining. [0003] Elhamifar & Vidal proposed a sparse subspace clustering model based on the self-expression property. The model uses the sparse self-expression ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2323G06F18/2136
Inventor 袁晓彤吴杰祺刘青山
Owner NANJING UNIV OF INFORMATION SCI & TECH
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