Multi-view data missing completion method for multi-manifold regularization non-negative matrix factorization

Abstract

The invention discloses a multi-view data missing complementing method for multi-manifold regularization non-negative matrix factorization, which comprises the following steps of: obtaining manifold and global clustering in which unmarked multi-view data tends to be consistent by utilizing a multi-manifold regularization non-negative matrix factorization algorithm through consistency assumption among multiple views; and constructing a multi-view collaborative discrimination model by adopting a view collaborative improved Gaussian mixture method. Pre-calibration of a cluster to which a sample belongs is realized by calculating the cluster relevancy level of the sample with missing data under a non-missing view angle; and establishing a missing data prediction model under a specific view angle by utilizing the consistency of multiple view angles in a low-dimensional space and adopting a multiple linear regression analysis method, thereby realizing accurate data completion under a multi-attribute missing condition. According to the method, large-scale labeled samples are not needed for training, the pre-defined category relation and related characteristics are avoided, and the understanding and discovering capacity of an existing multi-view mining technology for unlabeled multi-source data is improved.

Description

technical field [0001] The invention belongs to the technical field of machine learning for multi-view data as a research object, and specifically relates to a multi-manifold regularized non-negative matrix decomposition-based multi-view data missing complement method. Background technique [0002] With the rapid development of the Internet of Things and big data technology, the data collected by current applications has become more and more large and complex, and the multi-source and polymorphic characteristics of the data provide a basis for revealing things from different perspectives. different properties offer the possibility. For example, news event reports can be obtained from multiple news websites with different styles, different languages ​​in different countries, and various information forms such as video, audio, and pictures. In medical diagnosis, a large number of medical technologies (blood, urine, feces, and various medical instrument inspections) are used t...

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Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a multi-view data missing complement method based on multi-manifold regularized non-negative matrix decomposition, which can effectively avoid pre-defining category relationships and related features; In the traditional missing processing method, the estimation bias and statistical power loss caused by the deletion method are reduced, and the sample distribution distortion problem that may be caused by the single imputation method is reduced; the multi-view multi-attribute missing data is accurately completed in an unsupervised environment provides a new way of thinking

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