Abstract
We present an experimental evaluation of the subspaces obtained on positive data using the Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF) and Weighted Non-negative Matrix Factorization (WNMF) techniques in order to compare which technique provides a subspace that mantains the neighbourhood structure of the original space. Different distance metrics are used both in the original and the projected spaces in order to find which one is more adapted to our data. Results demonstrate that for our positive data (color histograms) a good candidate that preserves the original neighbourhood is NMF in conjunction with L 1 distance metric when the χ 2 metric is used in the original space. Since this is the most widely used distance metric when having histogram representations, our initial results seem to be relevant.
This work is supported by Comissionat per a Universitats i Recerca del Departament de la Presidencia de la Generalitat de Catalunya and Ministerio de Ciencia y Tecnologia grant TIC2000-0399-C02-01.
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Guillamet, D., Vitrià, J. (2003). An Experimental Evaluation of K-nn for Linear Transforms of Positive Data. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_37
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DOI: https://doi.org/10.1007/978-3-540-44871-6_37
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