SCUT_LLSF - implements the Scut thresholding technique from [2] 
  for the Linear Least Squares Fit classifier [3]
    THRESHOLD=SCUT_LLSF(A, Q, CLUSTERS, K, LABELS_TR, LABELS_TE, 
    MINF1, L, METHOD, STEPS, SVD_METHOD, CLSI_METHOD) returns 
    the vector of thresholds for the Linear Least Squares Fit 
    classifier for the collection [A Q]. A and Q define the 
    training and test parts of the validation set with labels 
    LABELS_TR and LABELS_TE respectively. CLUSTERS is  a 
    structure defining the classes, while MINF1 defines the 
    minimum F1 value and STEPS defines the number of steps 
    used during thresholding. 
    METHOD is the method used for the approximation of the 
    rank-l truncated SVD, with possible values:
        - 'clsi': Clustered Latent Semantic Indexing [4].
        - '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'. 
    [THRESHOLD, F, THRESHOLDS]=SCUT_LLSF(A, Q, CLUSTERS, K, 
    LABELS_TR, LABELS_TE, MINF1, L, METHOD, STEPS, SVD_METHOD, 
    CLSI_METHOD) returns also the best F1 value as well as the 
    matrix of thresholds for each step (row i corresponds to 
    step i).
 
    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. A Study of Thresholding Strategies for Text 
    Categorization. In Proc. 24th ACM SIGIR, pages 137–145, 
    New York, NY, USA, 2001. ACM Press.
    [3] 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. 
    [4] 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