BLOCK_NNDSVD - computes a non-negative rank-L approximation 
  of the input matrix using the Clustered Latent Semantic 
  Indexing Method [2] and the Non-Negative Double Singular 
  Value Decomposition Method [1].
    [X, Y]=BLOCK_NNDSVD(A, CLUSTERS, L, FUNC, ALPHA_VAL, 
    SVD_METHOD) computes a non-negative rank-L approximation 
    X*Y of the input matrix A with the Clustered Latent 
    Semantic Indexing Method [2], and the Non-Negative Double 
    Singular Value Decomposition Method [1], 
    using the cluster structure information from CLUSTERS [3].
    FUNC denotes the method used for the selection of the 
    number of factors from each cluster. Possible values for 
    FUNC:
        - 'f': Selection using a heuristic method from [2] 
          (see KS_SELECTION). 
        - 'f1': Same as 'f' but use at least one factor 
          from each cluster. 
        - 'equal': Use the same number of factors from 
          each cluster. 
    ALPHA_VAL is a value in [0, 1] used in the number of 
    factors selection heuristic [2]. Finally, SVD_METHOD 
    defines the method used for the computation of the SVD 
    (svds or propack).
 
    REFERENCES: 
    [1] C. Boutsidis and E. Gallopoulos. SVD-based 
    initialization: A head start on nonnegative matrix 
    factorization.  Pattern Recognition, Volume 41, 
    Issue 4, Pages 1350-1362, April 2008.
    [2] D. Zeimpekis and E. Gallopoulos. CLSI: A Flexible 
    Approximation Scheme from Clustered Term-Document 
    Matrices. In Proc. 5th SIAM International Conference 
    on Data Mining, pages 631–635, Newport Beach, California, 
    2005.
    [3] D. Zeimpekis and E. Gallopoulos. Document Clustering 
    using NMF based on Spectral Information. In Proc. Text Mining 
    Workshop 2008 held in conjunction with the 8th SIAM 
    International Conference on Data Mining, Atlanta, 2008. 
 
  Copyright 2011 Dimitrios Zeimpekis, Eugenia Maria Kontopoulou, 
                 Efstratios Gallopoulos