NDLI: Low rank sequential subspace clustering

Content Provider	IEEE Xplore Digital Library
Author	Yi Guo Junbin Gao Feng Li Tierney, S. Ming Yin
Copyright Year	2015
Description	Author affiliation: Sch. of Autom., Guangdong Univ. of Technol., Guangzhou, China (Ming Yin) \|\| Digital Productivity Flagship, CSIRO, North Ryde, NSW, Australia (Yi Guo) \|\| Sch. of Comput. & Math., Charles Sturt Univ., Bathurst, NSW, Australia (Junbin Gao; Tierney, S.) \|\| Qian Xuesen Lab. of Space Technol., Beijing, China (Feng Li)
Abstract	Sequential data are ubiquitous in data analysis. For example hyperspectral data taken from a drill hole in geology, high throughput X-ray diffraction measurements in materials research and EEG brain wave signals in neuroscience. The common feature of sequential data is that they are all acquired subject to one external variable such as location, time or temperature. The data evolve along the direction of that variable through several patterns and the “neighboring” data are very likely to share similar features. The purpose of the segmentation for sequential data is then to identify those sequentially continuous segments/patterns. We approach this problem by adopting the subspace clustering method and propose a novel algorithm called low rank sequential subspace clustering (LRSSC), inspired by another method called spatial subspace clustering (SpatSC). SpatSC finds the subspaces by data self-reconstruction with a sparsity constraint on reconstruction weights and promotes the spatial smoothness of the weights by fusion, the essential part in the fused LASSO. However, the subspace identification capability is limited due to the indeterminacy of the sparse regression in finding suitable samples to linearly reconstruct a given sample. This confuses the graph cut algorithm that produces the final clustering results on the weights. To overcome this drawback, we propose to use the low rank penalty instead of sparsity in learning phase to separate subspaces. This improves the subspace identification as well as the robustness to noise. To demonstrate its effectiveness, we test LRSSC on both simulated and real world data compared with SpatSC and other methods. The proposed algorithm is superior to others when noise level is very high.
Starting Page	1
Ending Page	8
File Size	1715845
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479919604
DOI	10.1109/IJCNN.2015.7280328
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-12
Publisher Place	Ireland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Nickel Zirconium
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

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