A method and device for multi-view clustering with incomplete cross graph matching

Abstract

The present application discloses a cross-graph matching incomplete multi-view clustering method and device. The method includes: establishing a missing value filling model for incomplete multi-modal data, where the multi-modal data includes webpage data or multimedia data; establishing incomplete multi-modal Cross-graph matching model for modal data; combined with the missing value filling model and the objective function of the cross-graph matching model, a multi-view clustering model with incomplete cross-graph matching is established; the multi-view clustering model with incomplete cross-graph matching is decomposed into three sub-models The problem includes optimizing the missing matrix E, solving the mapping space U and updating the connection matrix S; using an iterative algorithm to solve three sub-problems until the three sub-problems converge to obtain the optimal solution. While reducing the impact of missing data, the present application utilizes consistent and complementary information between modalities to improve the clustering effect.

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, some modal data 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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