NDLI: Soft-Hard Clustering for Multiview Data

Content Provider	IEEE Xplore Digital Library
Author	Tyagi, G. Patel, N. Sethi, I.
Copyright Year	2015
Description	Author affiliation: Sch. of Eng. & Comput. Eng., Oakland Univ., Rochester, MI, USA (Tyagi, G.; Patel, N.; Sethi, I.)
Abstract	With rapid advances in technology and connectivity, the capability to capture data from multiple sources has given rise to multiview learning wherein each object has multiple representations and a learned model, whether supervised or unsupervised, needs to integrate these different representations. Multiview learning has shown to yield better predictive and clustering models, it also is able to provide a better insight into relationships between different views for making better decisions. In this paper, we consider the problem of multiview clustering and present a soft-hard clustering approach. In our approach, all object views are first independently mapped into a unit hypercube via soft clustering. The mapped views are next integrated via a hard clustering approach to yield the final results. Both soft and hard clustering stages utilize k-means or its variant c-means, which makes our method suitable for large-scale data problems. Furthermore, additional parallelization of the view mapping stage in parallel is possible, thus making the method more attractive for large-scale data applications. The performance of the method using three benchmark data sets is demonstrated and a comparison with other published results shows our method mostly yields a slightly better performance.
Starting Page	464
Ending Page	469
File Size	893346
Page Count	6
File Format	PDF
ISBN	9781467366564
DOI	10.1109/IRI.2015.77
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hypercubes Accuracy Clustering algorithms Multimedia communication Visualization Vehicles Measurement
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 Library 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

Modeling centre-based hard and soft clustering for Y chromosome short tandem repeats (YSTR) data

Hard and soft updating centroids for clustering Y-short tandem repeats (Y-STR) data

Web Multimedia Object Clustering via Information Fusion

Exploiting side information in distance dependent Chinese restaurant processes for data clustering

On the structure of algorithm spaces

Visualization approach based on multimedia clustering

Comparison of algorithms for patent documents clusterization

Multi-source RSC model for multiple search result clustering

Clustering Social Event Images Using Kernel Canonical Correlation Analysis

Soft-Hard Clustering for Multiview Data

Similar Documents

Modeling centre-based hard and soft clustering for Y chromosome short tandem repeats (YSTR) data

Hard and soft updating centroids for clustering Y-short tandem repeats (Y-STR) data

Web Multimedia Object Clustering via Information Fusion

Exploiting side information in distance dependent Chinese restaurant processes for data clustering

On the structure of algorithm spaces

Visualization approach based on multimedia clustering

Comparison of algorithms for patent documents clusterization

Multi-source RSC model for multiple search result clustering

Clustering Social Event Images Using Kernel Canonical Correlation Analysis

Soft-Hard Clustering for Multiview Data