Partial Multi-View Clustering
نویسندگان
چکیده
Real data are often with multiple modalities or coming from multiple channels, while multi-view clustering provides a natural formulation for generating clusters from such data. Previous studies assumed that each example appears in all views, or at least there is one view containing all examples. In real tasks, however, it is often the case that every view suffers from the missing of some data and therefore results in many partial examples, i.e., examples with some views missing. In this paper, we present possibly the first study on partial multiview clustering. Our proposed approach, PVC, works by establishing a latent subspace where the instances corresponding to the same example in different views are close to each other, and similar instances (belonging to different examples) in the same view should be well grouped. Experiments on two-view data demonstrate the advantages of our proposed approach.
منابع مشابه
Multi-Task Multi-View Clustering for Non-Negative Data
Multi-task clustering and multi-view clustering have severally found wide applications and received much attention in recent years. Nevertheless, there are many clustering problems that involve both multi-task clustering and multi-view clustering, i.e., the tasks are closely related and each task can be analyzed from multiple views. In this paper, for non-negative data (e.g., documents), we int...
متن کاملMultiple Medoids based Multi-view Relational Fuzzy Clustering with Minimax Optimization
Multi-view data becomes prevalent nowadays because more and more data can be collected from various sources. Each data set may be described by different set of features, hence forms a multi-view data set or multi-view data in short. To find the underlying pattern embedded in an unlabelled multiview data, many multi-view clustering approaches have been proposed. Fuzzy clustering in which a data ...
متن کاملLow-rank Multi-view Clustering in Third-Order Tensor Space
The plenty information from multiple views data as well as the complementary information among different views are usually beneficial to various tasks, e.g., clustering, classification, de-noising. Multi-view subspace clustering is based on the fact that the multi-view data are generated from a latent subspace. To recover the underlying subspace structure, the success of the sparse and/or low-r...
متن کاملIncremental Minimax Optimization based Fuzzy Clustering for Large Multi-view Data
Incremental clustering approaches have been proposed for handling large data when given data set is too large to be stored. The key idea of these approaches is to find representatives to represent each cluster in each data chunk and final data analysis is carried out based on those identified representatives from all the chunks. However, most of the incremental approaches are used for single vi...
متن کاملRobust Kernelized Multi-View Self-Representations for Clustering by Tensor Multi-Rank Minimization
Most recently, tensor-SVD is implemented on multi-view self-representation clustering and has achieved the promising results in many real-world applications such as face clustering, scene clustering and generic object clustering. However, tensor-SVD based multi-view self-representation clustering is proposed originally to solve the clustering problem in the multiple linear subspaces, leading to...
متن کامل