Grassmann clustering

European Signal Processing Conference

Volumn , Issue , 2006, Pages

  Gruber, Peter ^a   Theis, Fabian J ^a  

^a UNIVERSITY OF REGENSBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CENTROID CALCULATION; CLOSED FORM; CLUSTER ASSIGNMENT; CLUSTERING METHODS; CODEBOOKS; DATA SETS; DISJOINT CLUSTERS; EUCLIDEAN SPACES; GRASSMANN MANIFOLD; GRASSMANNIAN; HIGH-DIMENSIONAL; K-MEANS; K-MEANS ALGORITHM; MULTIPLE SAMPLES; NONNEGATIVE MATRIX FACTORIZATION; PARTITIONAL CLUSTERING; PROJECTIVE SPACES;

GEOMETRY; SIGNAL PROCESSING;

ALGORITHMS;

EID: 84862626291     PISSN: 22195491     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. C.B. Barber, D.P. Dobkin, and H. Huhdanpaa. The quickhull algorithm for convex hull. Technical Report GCG53, The Geometry Center, University of Minnesota, Minneapolis, 1993.
2. C.M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995.
3. L. Bottou and Y. Bengio. Convergence properties of the kmeans algorithms. In Proc. NIPS 1994, pages 585-592. MIT Press, 1995.
4. P.S. Bradley and O.L. Mangasarian. k-plane clustering. Journal of Global Optimization, 16(1):23-32, 2000.
5. Sanjoy Dasgupta. How fast is k -means? Computational Learning Theory, 2777:735, 2003.
6. A. Edelman, T.A. Arias, and S.T. Smith. The geometry of algorithms with orthogonality constraints. SIAM Journal on Matrix Analysis and Applications, 20(2):303-353, 1999.
7. L. Parsons, E. Haque, and H. Liu. Subspace clustering for high dimensional data: a review. SIGKDD Explor. Newsl., 6(1):90-105, 2004.
8. S.Z. Selim and M.A. Ismail. K-means-type algorithms: a generalized convergence theorem and characterization of local optimality. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:81-87, 1984.