Proceedings ArticleDOI
Partial Multi-View Clustering using Graph Regularized NMF
Nishant Rai,Sumit Negi,Santanu Chaudhury,Om D. Deshmukh +3 more
- pp 2192-2197
Reads0
Chats0
TLDR
The proposed method, which is referred to as GPMVC (Graph Regularized Partial Multi-View Clustering), is compared against 7 baseline methods (including PVC) on 5 publicly available text and image datasets and outperforms all baselines.Abstract:
Real-world datasets consist of data representations (views) from different sources which often provide information complementary to each other. Multi-view learning algorithms aim at exploiting the complementary information present in different views for clustering and classification tasks. Several multi-view clustering methods that aim at partitioning objects into clusters based on multiple representations of the object have been proposed. Almost all of the proposed methods assume that each example appears in all views or at least there is one view containing all examples. In real-world settings this assumption might be too restrictive. Recent work on Partial View Clustering addresses this limitation by proposing a Non-negative Matrix Factorization based approach called PVC. Our work extends the PVC work in two directions. First, the current PVC algorithm is designed specifically for two-view datasets. We extend this algorithm for the k partial-view scenario. Second, we extend our k partial-view algorithm to include view specific graph laplacian regularization. This enables the proposed algorithm to exploit the intrinsic geometry of the data distribution in each view. The proposed method, which is referred to as GPMVC (Graph Regularized Partial Multi-View Clustering), is compared against 7 baseline methods (including PVC) on 5 publicly available text and image datasets. In all settings the proposed GPMVC method outperforms all baselines. For the purpose of reproducibility, we provide access to our code.read more
Citations
More filters
Journal ArticleDOI
Multi-view Clustering: A Survey
TL;DR: A large number of multi-view clustering algorithms are summarized, a taxonomy according to the mechanisms and principles involved is provided, and a few examples for how these techniques are used are given.
Journal ArticleDOI
Incomplete Multiview Spectral Clustering With Adaptive Graph Learning
TL;DR: The proposed method is the first work that exploits the graph learning and spectral clustering techniques to learn the common representation for incomplete multiview clustering and achieves the best performance in comparison with some state-of-the-art methods.
Journal ArticleDOI
Generalized Incomplete Multiview Clustering With Flexible Locality Structure Diffusion
TL;DR: A novel graph-regularized matrix factorization model is developed to preserve the local geometric similarities of the learned common representations from different views and the semantic consistency constraint is introduced to stimulate these view-specific representations toward a unified discriminative representation.
Posted Content
A Survey on Multi-View Clustering
TL;DR: This paper reviews the common strategies for combining multiple views of data and proposes a novel taxonomy of the MVC approaches, and discusses the relationships between MVC and multi-view representation, ensemble clustering, multi-task clustering), multi-View supervised and semi-supervised learning.
Journal ArticleDOI
Unified Embedding Alignment with Missing Views Inferring for Incomplete Multi-View Clustering
TL;DR: A locality-preserved reconstruction term is introduced to infer the missing views such that all views can be naturally aligned and a consensus graph is adaptively learned and embedded via the reverse graph regularization to guarantee the common local structure of multiple views.
References
More filters
Journal ArticleDOI
A collaborative framework for 3D alignment and classification of heterogeneous subvolumes in cryo-electron tomography
Oleg Kuybeda,Gabriel A. Frank,Alberto Bartesaghi,Mario J. Borgnia,Sriram Subramaniam,Guillermo Sapiro +5 more
TL;DR: The genetic identity of each virus particle present in the mixture can be assigned based solely on the structural information derived from single envelope glycoproteins displayed on the virus surface by the nuclear norm-based, collaborative alignment method presented here.
Journal ArticleDOI
Graph Regularized Nonnegative Matrix Factorization for Data Representation
TL;DR: In GNMF, an affinity graph is constructed to encode the geometrical information and a matrix factorization is sought, which respects the graph structure, and the empirical study shows encouraging results of the proposed algorithm in comparison to the state-of-the-art algorithms on real-world problems.
Journal ArticleDOI
Parameter-less Auto-weighted multiple graph regularized Nonnegative Matrix Factorization for data representation
TL;DR: In GNMF, an affinity graph is constructed to encode the geometrical information and a matrix factorization is sought, which respects the graph structure, and the empirical study shows encouraging results of the proposed algorithm in comparison to the state-of-the-art algorithms on real-world problems.
Proceedings Article
Co-regularized Multi-view Spectral Clustering
TL;DR: A spectral clustering framework is proposed that achieves this goal by co-regularizing the clustering hypotheses, and two co- regularization schemes are proposed to accomplish this.
Proceedings ArticleDOI
Multi-view clustering via joint nonnegative matrix factorization
TL;DR: This paper proposes a novel NMFbased multi-view clustering algorithm by searching for a factorization that gives compatible clustering solutions across multiple views and designs a novel and effective normalization strategy inspired by the connection between NMF and PLSA.