Masatran, Rajasekaran

IIT MadrasYahooBTech, IIIT Hyderabad.

A Latent-Variable Lattice Model

Abstract: Markov random field (MRF) learning is intractable, and its approximation algorithms are computationally expensive. We target a small subset of MRF that is used frequently in computer vision. We characterize this subset with three concepts: Lattice, Homogeneity, and Inertia; and design a non-markov model as an alternative. Our goal is robust learning from small datasets. Our learning algorithm uses vector quantization and, at time complexity O(u log(u)) for a dataset of u pixels, is much faster than that of general-purpose MRF.

A Marginal-Based Technique for Distribution Estimation

Abstract: Estimating a distribution over a vector random variable, given a source of independent random instances drawn from the distribution, is a standard problem in statistics. Frequently, the components have limited dependency between each other, and this can be exploited for estimation with fewer samples. We propose a novel technique that estimates the distribution efficiently, using one-dimensional marginals. Like naive bayes, our technique is suited to incremental estimation. Compared to the naive bayes assumption, our technique provides better accuracy, but only at higher dimensionality. Experiments on datasets of different dimensionality support our claims.