Presents FS-MWK++ and scalable SFS-MWK++ algorithms for unsupervised feature selection via stability of feature weights in Minkowski weighted k-means across a range of exponents, supported by theoretical analysis under noise and cluster assumptions.
Feature selection techniques for machine learning: a survey of more than two decades of research
2 Pith papers cite this work. Polarity classification is still indexing.
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2025 2verdicts
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The work identifies bands at 497 nm, 607 nm, and 895 nm that deliver large gains in material dissimilarity and perceptual separability on the H-City dataset compared with RGB.
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Scalable unsupervised feature selection via weight stability
Presents FS-MWK++ and scalable SFS-MWK++ algorithms for unsupervised feature selection via stability of feature weights in Minkowski weighted k-means across a range of exponents, supported by theoretical analysis under noise and cluster assumptions.
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CSNR and JMIM Based Spectral Band Selection for Reducing Metamerism in Urban Driving
The work identifies bands at 497 nm, 607 nm, and 895 nm that deliver large gains in material dissimilarity and perceptual separability on the H-City dataset compared with RGB.