Quantitative analysis of kernel properties in Kohonen's self-organizing map algorithm: Gaussian and difference of Gaussians neighborhoods

Liljeholm, Mimi; Lin, Andrew; Ożdżyński, Piotr; Beatty, Jackson

doi:10.1016/s0925-2312(02)00410-1

Cited by 2 publications

(1 citation statement)

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“…Recently, kernel functions have been applied to various kinds of methods such as support vector machine (SVM) [17], principal component analysis (PCA) [18][19][20][21], linear discriminate analysis (LDA) [22], and self-organizing map (SOM) [23]. Given a projection onto a higher dimensional space, nonlinear data generally vary linearly in the space.…”

Section: Kplsmentioning

confidence: 99%

Investigations of bagged kernel partial least squares (KPLS) and boosting KPLS with applications to near‐infrared (NIR) spectra

Shinzawa

Jiang

Ritthiruangdej

et al. 2006

Journal of Chemometrics

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The effects of ensemble learning methods, bagging and boosting, on kernel partial least squares (KPLS) regression are investigated. In bagged KPLS and boosting KPLS, a series of weak hypothesis with picked out samples from a training set are made with KPLS algorithm. Finally, a regression model is developed as an ensemble of these hypotheses. The abilities of bagged KPLS and boosting KPLSR are investigated with two near-infrared (NIR) spectroscopic data sets, NIR diffuse reflectance spectra of dried tobacco leaves and NIR transmission spectra of Thai fish sauces, by comparing with other methods, standard partial least squares (PLS), bagged PLS, and boosting PLS and KPLS. The results reveal that bagged KPLS and boosting KPLS yield superior regression performances to standard PLS. Especially, boosting KPLS indicates clear improvements over to PLS, bagged PLS boosting PLS, KPLS and bagged KPLS.

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