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High-dimensional variable selection when features are sparse

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STS - Statistical scalability

It is common in modern prediction problems for many predictor variables to be counts of rarely occurring events. This leads to design matrices in which a large number of columns are highly sparse. The challenge posed by such “rare features” has received little attention despite its prevalence in diverse areas, ranging from biology (e.g., rare species) to natural language processing (e.g., rare words). We show, both theoretically and empirically, that not explicitly accounting for the rareness of features can greatly reduce the effectiveness of an analysis. We next propose a framework for aggregating rare features into denser features in a flexible manner that creates better predictors of the response. An application to online hotel reviews demonstrates the gain in accuracy achievable by proper treatment of rare words. This is joint work with Xiaohan Yan.

This talk is part of the Isaac Newton Institute Seminar Series series.

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