Data clustering method for modal incomplete alignment

Abstract

The invention discloses a data clustering method for modal incomplete alignment. The method comprises: S1, acquiring a plurality of modal data sets, taking one modal data set as alignment modal data,and remaining analog non-alignment modal data; S2, respectively inputting each modal data set into one self-encoding network; S3, calculating a distance matrix of an alignment mode and a non-alignmentmode; S4, sending the distance matrix of the non-alignment modal data into a differentiable alignment module to calculate a prediction permutation matrix; S5, calculating a loss value by adopting a loss function; S6, performing back propagation based on the loss value to optimize the self-encoding network; S7, respectively inputting the modal data sets in the step S1 into the optimized self-encoding networks corresponding to the modal data sets; S8, obtaining a new prediction permutation matrix by adopting the execution modes of S3 and S4, and permuting the public representation output in thestep S7 by adopting the new prediction permutation matrix to obtain an aligned public representation; and S9, splicing the common representations output in the step S8, and then performing clusteringto obtain a clustering result.

Description

technical field [0001] The invention relates to data classification technology, in particular to a data clustering method oriented to incomplete alignment of modes. Background technique [0002] Data clustering is a kind of unsupervised machine learning method, which aims to divide the data into some aggregation classes according to the intrinsic properties of the data. The elements in each aggregation class have the same characteristics as much as possible, and the characteristics of different aggregation classes are as different as possible May be big. Since most real-world data are presented in the form of multiple modalities, multimodal data clustering performs clustering by exploring and exploiting the inherent correlation and invariance of data between different modalities. Generally speaking, most of the existing multimodal data clustering methods bridge the gap between different modalities by jointly learning a common representation of multiple modalities, and then ...

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But in these real-world scenarios, collecting complete and fully aligned multimoda...

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