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ECML
2007
Springer
14 years 2 months ago
Spectral Clustering and Embedding with Hidden Markov Models
Abstract. Clustering has recently enjoyed progress via spectral methods which group data using only pairwise affinities and avoid parametric assumptions. While spectral clustering ...
Tony Jebara, Yingbo Song, Kapil Thadani
PAMI
2011
13 years 2 months ago
Parallel Spectral Clustering in Distributed Systems
Spectral clustering algorithms have been shown to be more effective in finding clusters than some traditional algorithms such as k-means. However, spectral clustering suffers fro...
Wen-Yen Chen, Yangqiu Song, Hongjie Bai, Chih-Jen ...
ML
2010
ACM
193views Machine Learning» more  ML 2010»
13 years 2 months ago
On the eigenvectors of p-Laplacian
Spectral analysis approaches have been actively studied in machine learning and data mining areas, due to their generality, efficiency, and rich theoretical foundations. As a natur...
Dijun Luo, Heng Huang, Chris H. Q. Ding, Feiping N...
DATE
2008
IEEE
131views Hardware» more  DATE 2008»
14 years 2 months ago
Optimal High-Resolution Spectral Analyzer
This paper presents a new application field for the Goertzel algorithm. The test of mixed-signal circuits involves the generation and analysis of signals. A standard method for th...
A. Tchegho, Heinz Mattes, Sebastian Sattler
IGARSS
2010
13 years 5 months ago
Recent developments in sparse hyperspectral unmixing
This paper explores the applicability of new sparse algorithms to perform spectral unmixing of hyperspectral images using available spectral libraries instead of resorting to well...
Marian-Daniel Iordache, Antonio J. Plaza, Jos&eacu...