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Projection-based comprehensive multi-view clustering with smooth regularization
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AI
DOI：10.1016/j.asoc.2025.113025.png)
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Abstract
En 中文
By mining latent representations of data, subspace clustering methods can be more accurate and robust. However, such methods face the following limitations: they lack the ability to explicitly preserve the relationships between individuals within the original data cluster when constructing low dimensional latent representations. Specifically, during data dimensionality reduction, it cannot ensure that the retained information is relevant information between samples. Secondly, data dimensionality reduction may lead to the loss of key information within some samples, affecting the construction of self-representation matrices and reducing clustering performance. To solve these two problems, a new multi-view clustering method is proposed, Projection-based comprehensive multi-view clustering with smooth regularization (PCMCS). We design a smooth regular term for the projection matrix, which can make the data after dimensionality-reduced retain the grouping effect of the original data. Then, we capture the representation matrix of the original data and the data after dimensionality-reduced, and construct the resulting representation matrix as a tensor such that the two self-representation matrices are optimized with respect to each other, which to a certain extent neutralizes the interference of information loss, data redundancy, and noise in the construction of the representation matrices and improves the clustering performance. Experiments are conducted on 8 datasets, demonstrating the effectiveness of PCMCS.
Keywords:
Latent representation
Multi-view clustering
Projection
Grouping effect
Tensor
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