LLSF_MULTI - Linear Least Squares Fit for multi-label 
  collections [2]
    LABELS_AS=LLSF_MULTI(A, Q, CLUSTERS, LABELS, L, METHOD, 
    THRESHOLDS, 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). 
    THRESHOLDS is a vector of class threshold values, while 
    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, "Linear and 
    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