Learning robustness multi-view clustering method based on nonnegative dictionaries

Abstract

The invention discloses a learning robustness multi-visual angle clustering method based on nonnegative dictionaries, comprising: extracting characteristics of a data set including a plurality of sub-spaces under different visual angles; embedding characteristic learning in dictionary learning, and jointly learning a meaning projection matrix and nonnegative characteristic projection; adding consistency constraints and local geometrical constraints to learn common cluster labels shared by multiple visual angles, learning each visual angle meaning projection matrix, parameter expression matrix and multi-visual angle shared meaning projection matrix under a plurality of constrains, and completing multi-visual angle clustering. The method can explore common meaning labels shared by multiple visual angles, add consistency constrains, reduce difference between a single clustering label and a common meaning label, and meanwhile add the local geometrical constraints, allowing data with a similar structure to be divided in a same cluster with a greater probability.

Description

technical field [0001] The invention relates to the technical field of pattern recognition, in particular to a robust multi-view clustering method based on non-negative dictionary pair learning. Background technique [0002] In reality, many data have multiple modalities, for example, articles can be translated into multiple languages, news can be reported from multiple perspectives, and pictures can be described by multiple descriptors. In general, multi-view data can provide complementary and compatible information. Multi-view clustering, an unsupervised multi-view learning method, has attracted attention. Past work mainly falls into two categories: regression-based methods and subspace-based methods. Although these methods have achieved significant improvements, they are limited. Because these methods all assume that the given features are noise-free, these data can reveal the underlying cluster structure. But unfortunately, real-life features are often redundant and ...

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Problems solved by technology

But unfortunately, real-life features are often redundant and noisy, which greatly degrades the performance of the above methods

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