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We present regularized multiple density estimation (MDE) using the maximum entropy (MaxEnt) framework for multi-instance datasets. In this approach, bags of instances are represented as distributions using the principle of MaxEnt. We learn basis functions which span the space of distributions for jointly regularized density estimation. The basis functions are analogous to topics in a topic model....
Topic models have been proposed to model a collection of data such as text documents and images in which each object (e.g., a document) contains a set of instances (e.g., words). In many topic models, the dimension of the latent topic space (the number of topics) is assumed to be a deterministic unknown. The number of topics significantly affects the prediction performance and interpretability of...
Topic models are widely used in a variety of applications including document classification and computer vision. The number of topics in the model plays an important role in terms of accuracy. We consider the problem of estimating the number of topics. In [1], a convex optimization approach was proposed to solve the problem via a constrained nuclear norm minimization. A standard semidefinite programming...
In the past few years, probabilistic topic models have been developed and applied to problems in text document classification and computer vision. Such models provide a probabilistic framework for characterizing a corpus of documents (or images) in the bag-of-words representation. Key feature of such models is that a low dimensional representation is facilitated through latent topic variables. Most...
In this paper, we present a novel entropy estimator for a given set of samples drawn from an unknown probability density function (PDF). Counter to other entropy estimators, the estimator presented here is parametric. The proposed estimator uses the maximum entropy principle to offer anm-term approximation to the underlying distribution and does not rely on local density estimation. The accuracy of...
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