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