Analyzing non-negative matrix factorization for image classification

@article{Guillamet2002AnalyzingNM,
  title={Analyzing non-negative matrix factorization for image classification},
  author={David Guillamet and Bernt Schiele and Jordi Vitri{\`a}},
  journal={Object recognition supported by user interaction for service robots},
  year={2002},
  volume={2},
  pages={116-119 vol.2}
}
The Non-negative Matrix Factorization technique (NMF) has been recently proposed for dimensionality reduction. NMF is capable to produce a region- or part-based representation of objects and images. This paper experimentally compares NMF to Principal Component Analysis (PCA) in the context of image patch classification. A first finding is that the two techniques are complementary and that their respective performance is correlated to the with-in class scatter. This paper also analyses different… CONTINUE READING

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