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Is there a principled way to learn a probabilistic discriminative classifier from an unlabeled data set? We present a framework that simultaneously clusters the data and trains a discriminative classifier. We call it Regularized Information Maxi-mization (RIM). RIM optimizes an intuitive information-theoretic objective function which balances class… (More)

Clustering is a fundamental task in many vision applications. To date, most clustering algorithms work in a batch setting and training examples must be gathered in a large group before learning can begin. Here we explore incremental clustering, in which data can arrive continuously. We present a novel incremental model-based clustering algorithm based on… (More)

We consider the problem of extracting informative exemplars from a data stream. Examples of this problem include exemplar-based 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,… (More)

What type of algorithms and statistical techniques support learning from very large datasets over long stretches of time? We address this question through a memory bounded version of a variational EM algorithm that approximates inference in a topic model. The algorithm alternates two phases: "model building" and "model compression" in order to always… (More)

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