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- W1489135038 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.KeywordsPrincipal Component AnalysisProjected SpaceColor HistogramOriginal SpaceDistance MetricsThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
- W1489135038 created "2016-06-24" @default.
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- W1489135038 date "2003-01-01" @default.
- W1489135038 modified "2023-10-09" @default.
- W1489135038 title "An Experimental Evaluation of K-nn for Linear Transforms of Positive Data" @default.
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- W1489135038 doi "https://doi.org/10.1007/978-3-540-44871-6_37" @default.
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