LLSF_SINGLE - Linear Least Squares Fit for single-label 
  collections [2]
    LABELS_AS=LLSF_SINGLE(A, Q, CLUSTERS, LABELS, L, METHOD, 
    SVD_METHOD, CLSI_METHOD) classifies the columns of Q 
    with the Linear Least Squares Fit classifier [2] using 
    the pre-classified columns of matrix A with labels LABELS 
    (cell array of vectors of integers). CLUSTERS is a 
    structure defining the classes. METHOD is the method
    used for the approximation of the rank-l truncated SVD, 
    with possible values:
        - 'clsi': Clustered Latent Semantic Indexing [3].
        - 'cm': Centroids Method [1].
        - 'svd': Singular Value Decomosition.
    SVD_METHOD defines the method used for the computation 
    of the SVD, while CLSI_METHOD defines the method used for 
    the determination of the number of factors from each class 
    used in Clustered Latent Semantic Indexing in case METHOD 
    equals 'clsi'. 
 
    REFERENCES:
    [1] H. Park, M. Jeon, and J. Rosen. Lower Dimensional 
    Representation of Text Data Based on Centroids and Least 
    Squares. BIT Numerical Mathematics, 43(2):427–448, 2003.
    [2] Y. Yang and C. Chute. A Linear Least Squares Fit 
    Mapping Method for Information Retrieval from Natural 
    Language Texts. In Proc. 14th Conference on Computational 
    Linguistics, pages 447–453, Morristown, NJ, USA, 1992. 
    [3] D. Zeimpekis and E. Gallopoulos, "Non-Linear Dimensional 
    Reduction via Class Representatives for Text Classification".  
    In Proc. 2006 IEEE International Conference on Data Mining 
    (ICDM'06), Hong Kong, Dec. 2006.  
 
  Copyright 2011 Dimitrios Zeimpekis, Eugenia Maria Kontopoulou, 
                 Efstratios Gallopoulos
					
				

Return to main page