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ANIMC: A Soft Approach for Autoweighted Noisy and Incomplete Multiview Clustering | IEEE Journals & Magazine | IEEE Xplore

ANIMC: A Soft Approach for Autoweighted Noisy and Incomplete Multiview Clustering

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Impact Statement:Impact Statement—As an effective method to process data from multiple sources, multiview clustering has attracted more and more attention. However, most previous works ig...Show More

Abstract:

Multiview clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, w...Show More
Impact Statement:
Impact Statement—As an effective method to process data from multiple sources, multiview clustering has attracted more and more attention. However, most previous works ignore missing instances and noises in original multiview data, which limits their applications. By a soft approach, our proposed ANIMC effectively reduces the negative influence of missing instances and noises. Moreover, ANIMC outperforms other state-of-the-art works by about 20% in representative cases. With satisfactory performance on multiple real-world datasets, ANIMC has wide potential applications, including the analysis of multilingual document and image datasets.

Abstract:

Multiview clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multiview clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this article, we propose a novel autoweighted noisy and incomplete multiview clustering (ANIMC) approach via a soft autoweighted strategy and a doubly soft regular regression model. First, by designing adaptive semiregularized nonnegative matrix factorization, the soft autoweighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Second, by proposing \theta-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different \theta. Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm ...
Published in: IEEE Transactions on Artificial Intelligence ( Volume: 3, Issue: 2, April 2022)
Page(s): 192 - 206
Date of Publication: 01 October 2021
Electronic ISSN: 2691-4581

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