Kernel Feature Selection with Side Data Using a Spectral Approach
We address the problem of selecting a subset of the most relevant features from a set of sample data in cases where there are multiple (equally reasonable) solutions. In particular, this topic includes on one hand the introduction of hand-crafted kernels which emphasize certain desirable aspects of the data and, on the other hand, the suppression of one of the solutions given “side” data, i.e., when one is given information about undesired aspects of the data. Such situations often arise when there are several, even conflicting, dimensions to the data. For example, documents can be clustered based on topic, authorship or writing style; images of human faces can be clustered based on illumination conditions, facial expressions or by person identity, and so forth.