In this paper we present an application of single cluster visualization (SCV) a technique to visualize single clusters of high-dimensional data. This method maps a single cluster to the plane trying to preserve the relative distances of feature vectors to the corresponding prototype vector. Thus, fuzzy clustering results representing relative distances in the form of a partition matrix as well ...

Multi-view subspace clustering aims to discover the hidden structures from multiple views for robust clustering, and has been attracting considerable attention in recent years. Despite significant progress, most of previous multi-view algorithms are still faced with two limitations. First, they usually focus on consistency (or commonness) views, yet often lack ability capture cross-view inconsi...

Though subspace clustering, ensemble clustering, alternative clustering, and multiview clustering are different approaches motivated by different problems and aiming at different goals, there are similar problems in these fields. Here we shortly survey these areas from the point of view of subspace clustering. Based on this survey, we try to identify problems where the different research areas ...

Multi-view clustering takes diversity of multiple views (representations) into consideration. Multiple views may be obtained from various sources or different feature subsets and often provide complementary information to each other. In this paper, we propose a novel graph-based approach to integrate multiple representations to improve clustering performance. While original graphs have been wid...

In this paper we develop an algorithm for spectral clustering in the multi-view setting where there are two independent subsets of dimensions, each of which could be used for clustering (or classification). The canonical examples of this are simultaneous input from two sensory modalitites, where input from each sensory modality is considered a view, as well as web pages where the text on the pa...

In multiview learning, it is essential to assign a reasonable weight to each view according to the view importance. Thus, for multiview clustering task, a wise and elegant method should achieve clustering multiview data while learning the view weights. In this paper, we propose to explore a Laplacian rank constrained graph, which can be approximately as the centroid of the built graph for each ...

Multiview clustering algorithms allow leveraging information from multiple views of the data and therefore lead to improved clustering. A number of kernel based multiview clustering algorithms work by using the kernel matrices defined on the different views of the data. However, these algorithms assume availability of features from all the views of each example, i.e., assume that the kernel mat...

In recent years, collaborative filtering (CF) methods are important and widely accepted techniques are available for recommender systems. One of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. However, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. With the dev...

In 3D object detection and recognition, an object of interest is subject to changes in view as well as in illumination and shape. For image classification purpose, it is desirable to derive a representation in which intrinsic characteristics of the object are captured in a low dimensional space while effects due to artifacts are reduced. In this paper, we propose a method for view-based unsuper...

Learning from multi-view data is important in many applications. In this paper, we propose a novel convex subspace representation learning method for unsupervised multi-view clustering. We first formulate the subspace learning with multiple views as a joint optimization problem with a common subspace representation matrix and a group sparsity inducing norm. By exploiting the properties of dual ...