An efficient particle filtering technique on the grassmann manifold

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2010, Pages 3838-3841

  Rentmeesters, Quentin ^a   Absil, P A ^a   Van Dooren, Paul ^a   Gallivan, Kyle ^b   Srivastava, Anuj ^b  

^a UNIVERSITÉ CATHOLIQUE DE LOUVAIN   (Belgium)

^b FLORIDA STATE UNIVERSITY   (United States)

Author keywords

Grassmann manifold; Particle filtering; Time varying subspace learning

Indexed keywords

IMAGE PROCESSING; MONTE CARLO METHODS; STOCHASTIC MODELS;

FILTERING TECHNIQUE; GRASSMANN MANIFOLD; PARTICLE FILTERING; SIGNAL AND IMAGE PROCESSING; STOCHASTICS; SUBSPACE LEARNING; SUBSPACE TRACKING; TIME VARYING; TIME-VARYING SUBSPACE LEARNING; TRACKING METHOD;

STOCHASTIC SYSTEMS;

EID: 78049374604     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2010.5495828     Document Type: Conference Paper

References (5)

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3. P.-A. Absil, R. Mahony, and R. Sepulchre, Optimization algorithms on matrix manifolds, Princeton University Press, Princeton, NJ, 2008.
4. Kyle Gallivan, Anuj Srivastava, Xiuwen Liu, and Paul Van Dooren, "Efficient algorithms for inferences on Grassmann manifolds," in Proceedings of 12 th IEEE Workshop on Statistical Signal Processing, 2003, pp. 315-318.
5. Alan Edelman, Tomás A. Arias, and Steven T. Smith, "The geometry of algorithms with orthogonality constraints," SIAM J. Matrix Anal. Appl., vol. 20, no. 2, pp. 303-353 (electronic), 1999.