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MKPLS approach: switching strategies for the non-linear multi-kernel PLSR

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Abstract

We present two strategies to determine the kernel switching order for the non-linear multi-kernel PLSR algorithm. The multi-kernel PLS (MKPLS) algorithm builds upon a one kernel PLSR which uses a kernel matrix to hold the inner products of the projection of the independent data set onto a feature space. After building a PLSR model, MKPLS deflates the kernel matrix so that only that part which cannot be predicted by the model remains. This remainder is projected onto a different feature space and a new PLSR model is built. The switching algorithms presented for this approach address two questions: which kernel should be used at each iteration and; how many factors should be extracted before switching to another kernel.

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Authors and Affiliations

  1. Departamento de Informática, Pontifícia Universidade Católica do Rio de Janeiro, Rua Marquês de São Vicente 225, Gávea, 22553-900, Rio de Janeiro, Brazil

    Raúl Rentería, Ruy Milidiú & Rafael Souza

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  1. Raúl Rentería
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  2. Ruy Milidiú
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  3. Rafael Souza
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Correspondence to Raúl Rentería.

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Rentería, R., Milidiú, R. & Souza, R. MKPLS approach: switching strategies for the non-linear multi-kernel PLSR. Computational Statistics 22, 323–330 (2007). https://doi.org/10.1007/s00180-007-0040-5

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  • DOI: https://doi.org/10.1007/s00180-007-0040-5

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