Efficient spectral feature selection with minimum redundancy

Spectral feature selection identifies relevant features by

measuring their capability of preserving sample similarity.

It provides a powerful framework for both supervised

and unsupervised feature selection, and has been

proven to be effective in many real-world applications.

One common drawback associated with most existing

spectral feature selection algorithms is that they evaluate

features individually and cannot identify redundant

features. Since redundant features can have significant

adverse effect on learning performance, it is necessary

to address this limitation for spectral feature selection.

To this end, we propose a novel spectral feature selection

algorithm to handle feature redundancy, adopting

an embedded model. The algorithm is derived from

a formulation based on a sparse multi-output regression

with a L2;1-norm constraint. We conduct theoretical

analysis on the properties of its optimal solutions,

paving the way for designing an efficient path-following

solver. Extensive experiments show that the proposed

algorithm can do well in both selecting relevant features

and removing redundancy.