NDLI: Robust clustering of multi-type relational data via a heterogeneous manifold ensemble

Content Provider	IEEE Xplore Digital Library
Author	Jun Hou Nayak, R.
Copyright Year	2015
Description	Author affiliation: Sch. of Electr. Eng. & Comput. Sci., Queensland Univ. of Technol., Brisbane, QLD, Australia (Jun Hou; Nayak, R.)
Abstract	High-Order Co-Clustering (HOCC) methods have attracted high attention in recent years because of their ability to cluster multiple types of objects simultaneously using all available information. During the clustering process, HOCC methods exploit object co-occurrence information, i.e., inter-type relationships amongst different types of objects as well as object affinity information, i.e., intra-type relationships amongst the same types of objects. However, it is difficult to learn accurate intra-type relationships in the presence of noise and outliers. Existing HOCC methods consider the p nearest neighbours based on Euclidean distance for the intra-type relationships, which leads to incomplete and inaccurate intra-type relationships. In this paper, we propose a novel HOCC method that incorporates multiple subspace learning with a heterogeneous manifold ensemble to learn complete and accurate intra-type relationships. Multiple subspace learning reconstructs the similarity between any pair of objects that belong to the same subspace. The heterogeneous manifold ensemble is created based on two-types of intra-type relationships learnt using p-nearest-neighbour graph and multiple subspaces learning. Moreover, in order to make sure the robustness of clustering process, we introduce a sparse error matrix into matrix decomposition and develop a novel iterative algorithm. Empirical experiments show that the proposed method achieves improved results over the state-of-art HOCC methods for FScore and NMI.
Starting Page	615
Ending Page	626
File Size	521038
Page Count	12
File Format	PDF
ISBN	9781479979646
DOI	10.1109/ICDE.2015.7113319
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-13
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manifolds Matrix decomposition Noise Robustness Sparse matrices Web pages Clustering algorithms
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Robust Ensemble Clustering by Matrix Completion

Sparse manifold embedding Tri-factor Nonnegative Matrix Factorization

Nonnegative Matrix Tri-factorization Based High-Order Co-clustering and Its Fast Implementation

Simultaneous clustering of multi-type relational data via symmetric nonnegative matrix tri-factorization

Multi-view Clustering via Multi-manifold Regularized Nonnegative Matrix Factorization

Nonnegative Matrix Factorization With Regularizations

On the clustering of large-scale data: A matrix-based approach

A New Algorithm Estimating the Mixing Matrix for the Sparse Component Analysis

Robust non-negative matrix factorization via joint sparse and graph regularization

Robust clustering of multi-type relational data via a heterogeneous manifold ensemble

Similar Documents

Robust Ensemble Clustering by Matrix Completion

Sparse manifold embedding Tri-factor Nonnegative Matrix Factorization

Nonnegative Matrix Tri-factorization Based High-Order Co-clustering and Its Fast Implementation

Simultaneous clustering of multi-type relational data via symmetric nonnegative matrix tri-factorization

Multi-view Clustering via Multi-manifold Regularized Nonnegative Matrix Factorization

Nonnegative Matrix Factorization With Regularizations

On the clustering of large-scale data: A matrix-based approach

A New Algorithm Estimating the Mixing Matrix for the Sparse Component Analysis

Robust non-negative matrix factorization via joint sparse and graph regularization

Robust clustering of multi-type relational data via a heterogeneous manifold ensemble