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Abstract |
Feature selection is the task of choosing a small subset of features that is sufficient to predict the target labels well. Here, instead of trying to directly determine which features are better, we attempt to learn the properties of good features. For this purpose we assume that each feature is represented by a set of properties, referred to as meta-features. This approach enables prediction of the quality of features without measuring their value on the training instances. We use this ability to devise new selection algorithms that can efficiently search for new good features in the presence of a huge number of features, and to dramatically reduce the number of feature measurements needed. We demonstrate our algorithms on a handwritten digit recognition problem and a visual object category recognition problem. In addition, we show how this novel viewpoint enables derivation of better generalization bounds for the joint learning problem of selection and classification, and how it contributes to a better understanding of the problem. Specifically, in the context of object recognition, previous works showed that it is possible to find one set of features which fits most object categories (aka a universal dictionary). Here we use our framework to analyze one such universal dictionary and find that the quality of features in this dictionary can be predicted accurately by its meta-features. |
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