Spectral Clustering with Local Projection Distance Measurement
Joint Authors
Diao, Chen
Zhang, Ai-Hua
Wang, Bin
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-04-19
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Constructing a rational affinity matrix is crucial for spectral clustering.
In this paper, a novel spectral clustering via local projection distance measure (LPDM) is proposed.
In this method, the Local-Projection-Neighborhood (LPN) is defined, which is a region between a pair of data, and other data in the LPN are projected onto the straight line among the data pairs.
Utilizing the Euclidean distance between projective points, the local spatial structure of data can be well detected to measure the similarity of objects.
Then the affinity matrix can be obtained by using a new similarity measurement, which can squeeze or widen the projective distance with the different spatial structure of data.
Experimental results show that the LPDM algorithm can obtain desirable results with high performance on synthetic datasets, real-world datasets, and images.
American Psychological Association (APA)
Diao, Chen& Zhang, Ai-Hua& Wang, Bin. 2015. Spectral Clustering with Local Projection Distance Measurement. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1074837
Modern Language Association (MLA)
Diao, Chen…[et al.]. Spectral Clustering with Local Projection Distance Measurement. Mathematical Problems in Engineering No. 2015 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1074837
American Medical Association (AMA)
Diao, Chen& Zhang, Ai-Hua& Wang, Bin. Spectral Clustering with Local Projection Distance Measurement. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1074837
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1074837