Clustering data with both numeric and categorical attributes is of great importance as such are ubiquitous in real-world problems. Multi-view learning approaches have proven to be more effective having better generalisation ability compared single-view many However, most the existing clustering algorithms developed for mixed single-view. In this research, we propose a novel multi-view algorithm...

Multi-view data is common in a wide variety of application domains. Properly exploiting the relations among different views is helpful to alleviate the difficulty of a learning problem of interest. To this end, we propose an extended Probabilistic Latent Semantic Analysis (PLSA) model for multi-view clustering, named Co-regularized PLSA (CoPLSA). CoPLSA integrates individual PLSAs in different ...

Multi-view subspace clustering (MVSC) optimally integrates multiple graph structure information to improve performance. Recently, many anchor-based variants are proposed reduce the computational complexity of MVSC. Though achieving considerable acceleration, we observe that most them adopt fixed anchor points separating from subsequential construction, which may adversely affect In addition, po...

Deep Multi-view Subspace Clustering is a powerful unsupervised learning technique for clustering multi-view data, which has achieved significant attention during recent decades. However, most current methods rely on self-expressive layers to obtain the ultimate results, where size of matrix increases quadratically with number input data points, making it difficult handle large-scale datasets. M...

Exploiting the information from multiple views can improve clustering accuracy. However, most existing multi-view clustering algorithms are nonconvex and are thus prone to becoming stuck into bad local minima, especially when there are outliers and missing data. To overcome this problem, we present a new multi-view self-paced learning (MSPL) algorithm for clustering, that learns the multi-view ...

Multi-view clustering, which aims to improve the clustering performance by exploring the data’s multiple representations, has become an important research direction. Graph based methods have been widely studied and achieve promising performance for multi-view clustering. However, most existing multi-view graph based methods perform clustering on the fixed input graphs, and the results are depen...

In many clustering problems, we have access to multiple views of the data each of which could be individually used for clustering. Exploiting information from multiple views, one can hope to find a clustering that is more accurate than the ones obtained using the individual views. Often these different views admit same underlying clustering of the data, so we can approach this problem by lookin...

This work considers probability models for partitions of a set of n elements using a predictive approach, i.e., models that are specified in terms of the conditional probability of either joining an already existing cluster or forming a new one. The inherent structure can be motivated by resorting to hierarchical models of either parametric or nonparametric nature. Parametric examples include t...

With advances in information acquisition technologies, multi-view data become ubiquitous. Multi-view learning has thus become more and more popular in machine learning and data mining fields. Multi-view unsupervised or semi-supervised learning, such as co-training, co-regularization has gained considerable attention. Although recently, multi-view clustering (MVC) methods have been developed rap...