Budgeted Nonparametric Learning from Data Streams

We consider the problem of extracting informative exemplars from a data stream. Examples of this problem include exemplarbased clustering and nonparametric inference such as Gaussian process regression on massive data sets. We show that these problems require maximization of a submodular function that captures the informativeness of a set of exemplars, over a data stream. We develop an efficient algorithm, StreamGreedy, which is guaranteed to obtain a constant fraction of the value achieved by the optimal solution to this NP-hard optimization problem. We extensively evaluate our algorithm on large real-world data sets.

• Type: Conference
• Year: 2010
• Where: ICML
• Authors: Ryan Gomes, Andreas Krause
• Keywords: Data Sets, Data Stream, ICML 2010, Machine Learning, Massive Data Sets