A Study on Multi-Scale Kernel Optimisation via Centered Kernel-Target Alignment

Pérez-Ortiz, María; Gutiérrez, Pedro Antonio; Sánchez‐Monedero, Javier; Hervás-Martı́nez, César

doi:10.1007/s11063-015-9471-0

Cited by 6 publications

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References 27 publications

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“…The algorithmic approach followed for optimising the kernel parameters in each step is the one proposed in [34], where the concept of kernel-target alignment is used, optimising it through a gradient-descent strategy. In this case, the gradient-descent approach is used twice for optimising the kernel: firstly using the ideal supervised knowledge (to set an appropriate initial solution) and secondly using labelled and unlabelled data (to refine the previous solution).…”

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Semi-supervised learning for ordinal Kernel Discriminant Analysis

et al. 2016

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Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by an user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing 1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and 2) the advantage of computing distances in the feature space induced by the kernel function.

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