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Classical generative models in unsupervised learning intend to maximize $p(X)$. In practice, samples may have multiple representations caused by various transformations, measurements, and so on. Therefore, it is crucial to integrate information from different representations, and lots of models have been developed. However, most of them fail to incorporate the prior information about data distribution $p(X)$ to distinguish representations. In this article, we propose a novel clustering framework that attempts to maximize the joint probability of data and parameters. Under this framework, the prior distribution can be employed to measure the rationality of diverse representations. $K$ -means is a special case of the proposed framework. Meanwhile, a specific clustering model considering both multiple kernels and multiple views is derived to verify the validity of the designed framework and model. [ABSTRACT FROM AUTHOR]

As to unsupervised learning, most discriminative information is encoded in the cluster labels. To obtain the pseudo labels, unsupervised feature selection methods usually utilize spectral clustering to generate them. Nonetheless, two related disadvantages exist accordingly: 1) the performance of feature selection highly depends on the constructed Laplacian matrix and 2) the pseudo labels are obtained with mixed signs, while the real ones should be nonnegative. To address this problem, a novel approach for unsupervised feature selection is proposed by extending orthogonal least square discriminant analysis (OLSDA) to the unsupervised case, such that nonnegative pseudo labels can be achieved. Additionally, an orthogonal constraint is imposed on the class indicator to hold the manifold structure. Furthermore, $\ell _{2,1}$ regularization is imposed to ensure that the projection matrix is row sparse for efficient feature selection and proved to be equivalent to $\ell _{2,0}$ regularization. Finally, extensive experiments on nine benchmark data sets are conducted to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The conventional subspace clustering method obtains explicit data representation that captures the global structure of data and clusters via the associated subspace. However, due to the limitation of intrinsic linearity and fixed structure, the advantages of prior structure are limited. To address this problem, in this brief, we embed the structured graph learning with adaptive neighbors into the deep autoencoder networks such that an adaptive deep clustering approach, namely, autoencoder constrained clustering with adaptive neighbors (ACC_AN), is developed. The proposed method not only can adaptively investigate the nonlinear structure of data via a parameter-free graph built upon deep features but also can iteratively strengthen the correlations among the deep representations in the learning process. In addition, the local structure of raw data is preserved by minimizing the reconstruction error. Compared to the state-of-the-art works, ACC_AN is the first deep clustering method embedded with the adaptive structured graph learning to update the latent representation of data and structured deep graph simultaneously. [ABSTRACT FROM AUTHOR]