Large-scale multi-view data self-dimension-reduction K-means algorithm and system

Abstract

The invention relates to a large-scale multi-view data self-dimension-reduction K-means algorithm and a system, and belongs to the technical field of information processing, and the method comprises the steps: 1, carrying out the normalization of data with different features, and enabling all data to be in a range of [-1, 1]; 2, initializing; 3, optimizing the algorithm; and 4, using a data set tooptimize the algorithm according to the algorithm until the algorithm is finally converged to obtain a final clustering result, measuring the clustering effect by using the interaction information entropy and the purity, and repeating the step 3 by selecting different initial values, and removing the average value of the result to complete the experiment. The relationship between the features andthe clustering targets is fully considered; information complementation among different views is utilized, self-dimension reduction of high-dimensional data is achieved by searching for an optimal subspace on a single view, a loss function is reconstructed through non-negative matrix factorization (NMF), the different views share the same clustering indication matrix, therefore, multi-view information complementation is achieved, and clustering of large-scale multi-view data is completed.

Description

technical field [0001] The invention belongs to the technical field of information processing, and in particular relates to a large-scale multi-view data self-dimension reduction K-means algorithm and system. Background technique [0002] In the era of big data, the data of the same entity may come from different data sources and thus can be expressed in multiple forms, each of which has different distribution, scale and density. For example: when describing a person speaking, voice, facial expression, and lip changes can all be used as features; in the field of image recognition, images can extract various features such as color, outline, and key points. The heterogeneous feature data generated by observing the same object from different angles is called multi-view data. [0003] Unsupervised clustering of large-scale data is one of the main tasks of data mining. Studies have shown that effective information complementation between multi-view data can greatly improve clus...

AI Technical Summary

Problems solved by technology

However, the K-means algorithm treats all features equally, is very sensitive to redundant features, and is prone to clustering errors.

If dimensionality reduction is performed separately for different views, the information complementarity between views will be ignored

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