Multi-view clustering method and device with incomplete cross graph matching

Abstract

The invention discloses a multi-view clustering method and device with incomplete cross graph matching, and the method comprises the steps: building a missing value filling model of incomplete multi-modal data, wherein the multi-modal data comprises webpage data or multimedia data; establishing a cross graph matching model of the incomplete multi-modal data; in combination with target functions of the missing value filling model and the cross graph matching model, establishing a cross graph matching incomplete multi-view clustering model; decomposing the multi-view clustering model with incomplete cross graph matching into three sub-problems, including optimization of a missing matrix E, solving of a mapping space U and updating of a connection matrix S; and solving the three sub-problems by adopting an iterative algorithm until the three sub-problems converge, and obtaining an optimal solution. According to the method and device, the clustering effect is improved by utilizing consistent and complementary information among modals while the influence of missing data is reduced.

Description

technical field [0001] The present application relates to the technical field of image clustering, in particular to a method and device for multi-view clustering with incomplete cross-graph matching. Background technique [0002] In the era of big data, the types of data collection channels and feature extraction are becoming more and more diverse, so that the same object can be described from multiple data sources and features to generate multi-modal data. A hyperlink pointing to the page is described; a multimedia segment data can be described by its video and audio signals simultaneously. In practical applications, due to the time-consuming and laborious label collection, only a small amount of supervised information can often be collected, and the multi-modal semi-supervised clustering method can combine limited supervised information with a large amount of unsupervised information to learn, which greatly improves the accuracy of clustering. class effect. [0003] Howe...

AI Technical Summary

Problems solved by technology

[0003] However, in practical applications, due to the temporary failure of the data collector or human error, the data of some modalities is missing, and incomplete multi-view data are often obtained.

Most of the existing multimodal clustering algorithms are designed based on complete data, and cannot directly deal with incomplete multimodal data. Therefore, incomplete multimodal clustering came into being, aiming at reducing the impact of missing data and utilizing modal Consistent and complementary information between states to improve the clustering effect

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