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...

Many real-world datasets are comprised of different representations or views which often provide information complementary to each other. To integrate information from multiple views in the unsupervised setting, multiview clustering algorithms have been developed to cluster multiple views simultaneously to derive a solution which uncovers the common latent structure shared by multiple views. In...

All clustering methods have to assume some cluster relationship among the data objects that they are applied on. Similarity between a pair of objects can be defined either explicitly or implicitly. In this paper, we introduce a novel multi-viewpoint based similarity measure and two related clustering methods. The major difference between a traditional dissimilarity/similarity measure and ours i...

Uncovering community structure is a core challenge in social network analysis. This is a significant challenge for large networks where there is a single type of relation in the network (e.g. friend or knows). In practice there may be other types of relation, for instance demographic or geographic information, that also reveal network structure. Uncovering structure in such multi-relational net...

Most clustering algorithms produce a single clustering solution. This is inadequate for many data sets that are multi-faceted and can be grouped and interpreted in many different ways. Moreover, for high-dimensional data, different features may be relevant or irrelevant to each clustering solution, suggesting the need for feature selection in clustering. Features relevant to one clustering inte...

The maturity of structured knowledge bases and semantic resources has contributed to the enhancement of document clustering algorithms, that may take advantage of conceptual representations as an alternative for classic bag-of-words models. However, operating in the semantic space is not always the best choice in those domain where the choice of terms also matters. Moreover, users are usually r...

When multiple views of data are available for a set of subjects, co-clustering aims to identify subject clusters that agree across the different views. We explore the problem of co-clustering when the underlying clusters exist in different subspaces of each view. We propose a proximal alternating linearized minimization algorithm that simultaneously decomposes multiple data matrices into sparse...

We present a framework for combining automated and interactive visual analysis techniques for use on high-resolution biomechanical data. Analyzing the complex 3D motion of, e.g., pigs chewing or bats flying, can be enhanced by providing investigators with a multi-view interface that allows interaction across multiple modalities and representations. In this paper, we employ nonlinear dimensional...

In this paper we propose and investigate the possibilities inherent in a new, unsupervised approach to multi-view face recognition, which can be formulated mathematically as a problem of partitioning of proximity data, obtained from multi-view face image sequences. The proposed approach is implemented in two novel pairwise clustering algorithms, CAR1 and CAR2, which partition the input data int...

The data today is towards more observations and very high dimensions. Large high-dimensional data are usually sparse and contain many classes/clusters. For example, large text data in the vector space model often contains many classes of documents represented in thousands of terms. It has become a rule rather than the exception that clusters in high-dimensional data occur in subspaces of data, ...