Abstract
Dimensionality reduction procedures such as principal component analysis and the maximum margin criterion discriminant are special cases of a weighted maximum variance (WMV) approach. We present a simple two parameter version of WMV that we call 2P-WMV. We study the classification error given by the 1-nearest neighbor algorithm on features extracted by our and other dimensionality reduction methods on several real datasets. Our results show that our method yields the lowest average error across the datasets with statistical significance.
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Turki, T., Roshan, U. (2014). Weighted Maximum Variance Dimensionality Reduction. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-Lopez, J.A., Salas-Rodríguez, J., Suen, C.Y. (eds) Pattern Recognition. MCPR 2014. Lecture Notes in Computer Science, vol 8495. Springer, Cham. https://doi.org/10.1007/978-3-319-07491-7_2
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DOI: https://doi.org/10.1007/978-3-319-07491-7_2
Publisher Name: Springer, Cham
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