Although graph-inspired clustering methods have achieved impressive success in the area of multi-view data analysis, current methods still face several challenges. First, classical graph construction approaches are computationally expensive in terms of both time and space for large-scale data. Second, fusing graphs from different views remains a challenge. Third, most existing methods require additional post-processing steps to generate the label assignment matrix. To address the above challenges, this paper proposes a novel multi-view clustering algorithm, called latent information-guided one-step multi-view fuzzy clustering based on cross-view anchor graph. Specifically, we introduce an efficient cross-view anchor graph learning approach to construct the similarity matrix of multi-view data and extract latent information from the graph, which reduces the computational complexity of graph learning and inspires the optimization of the consensus membership matrix. Following that, our one-step multi-view fuzzy clustering algorithm can directly generate the final clustering result in an effective and straightforward manner. Furthermore, during the optimization process, the proposed method balances consensus matrix learning and view-specified membership exploration via a self-tuned weighting mechanism. The comprehensive experimental analysis demonstrates the superiority of our approach over the state-of-the-art approaches. 

Latent information-guided one-step multi-view fuzzy clustering based on cross-view anchor graph

Editorial paper for pattern recognition letters VSI on multi-view representation learning and multi-modal information representation

Trace ratio criterion for multi-view discriminant analysis

Multiview subspace learning (MvSL) has attracted extensive research interest in many real-world applications, e.g., pattern recognition and computer vision. Existing MvSL approaches seek a discriminant common subspace by simultaneously maximizing the arithmetic mean of the between-class covariance and minimizing the global average of the within-class covariance. However, the arithmetic mean normally assigns equal weight to all between-class distances. Then, classes with small distances overlap with each other, and the largest between-class distance dominates the common subspace. In addition, the global average of within-class covariance (which is computed by the sum of within-class distances for all classes) significantly differs from the within-class covariance of a single class. This will inevitably cause the internal details of each class to be ignored and lead to lower recognition accuracy. To circumvent these issues, we develop a productive strategy to take more consideration of the relationship between any two classes and propose a novel Multiview Latent Structure Learning (MvLSL) model, which incorporates the weighted harmonic mean of pairwise between-class scatter and pairwise within-class scatter. Due to the adjustable dimensions of the common subspace, MvLSL can be naturally extended for dimensionality reduction (DR), and we utilize a reconstruction term to suppress data degradation and improve the DR performance. Our models are tested on six benchmark datasets with five evaluation criteria and compared with existing state-of-the-art methods, indicating our models’ effectiveness and superiority. 

Multiview Latent Structure Learning: Local structure-guided cross-view discriminant analysis

In traditional machine learning, there is often a discrepancy in data distribution between the source and target domains. Domain adaptation (DA) was proposed to learn the robust calssifier for target domain by using konwledge from different source domains. Most DA methods focus on only the geometric structure of the data or statistical properties to reduce the differences between domains. The complementarity of these two aspects is ignored, which causes the problem of domain underadaptation to some degree. In this paper, we propose latent space search (LSS) approach for domain adaptation that consider both geometric and statistical properties. LSS consists of two parts: latent subspace learning and space search subspace learning. In the latent subspace, the geometric and statistical properties of the data are preserved by a low-rank coupled projection as well as joint distribution of the data. For the solution, an iterative feedback approach is used to obtain a robust subspace. Furthermore, to improve the discriminability of the subspace, the space search optimization algorithm is used to reconstruct the latent subspace, so that the source domain and the target domain can interleave well in the subspace. A comparative study illustrates that the proposed LSS has better performance than other state-of-art models reported in the literature. 

Latent space search approach for domain adaptation

Weighted multi-view common subspace learning method

Recent unsupervised dimension reduction algorithms use similarity graphs between data point pairs to preserve local structure while reducing dimension. However, the time complexity of these methods is proportional to the square of the number of samples, which limits their application to large-scale datasets. Moreover, the square Euclidean calculation criterion between sample point pairs will magnify the bad influence of outliers on the graph. In addition, these methods only preserve the local structure while losing other important structural information. To this end, we propose a fast adaptive unsupervised projection model termed Fast and Robust Unsupervised Dimensionality Reduction with Adaptive Bipartite Graph (FRUDR-ABG), which uses a few anchor points and sample points to build a bipartite graph to preserve the local geometric structure of the data to reduce the running time and improve efficiency. We propose a criterion based on the l2,1 norm to calculate the distance between anchor points and data points to reduce the negative influence of outliers on graph construction. A practical strategy is also proposed to realize joint learning of global and local structures. According to the characteristics of graph construction and dimensionality reduction adaptive learning in the algorithm, we design an iterative reweighting method to solve the model. Experimental results on several benchmark datasets show that FRUDR-ABG has higher efficiency and recognition performance than existing unsupervised dimensionality reduction methods. 

Fast and Robust Unsupervised Dimensionality Reduction with Adaptive Bipartite Graphs

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Papers

Weighted multi-view common subspace learning method

Trace ratio criterion for multi-view discriminant analysis

Multiview Latent Structure Learning: Local structure-guided cross-view discriminant analysis

Fast and Robust Unsupervised Dimensionality Reduction with Adaptive Bipartite Graphs