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- Yihua Chen, Eric K. Garcia, Maya R. Gupta, Ali Rahimi, Luca Cazzanti
- Journal of Machine Learning Research
- 2009

This paper reviews and extends the field of similarity-based classification, presenting new analyses, algorithms, data sets, and a comprehensive set of experimental results for a rich collection of classification problems. Specifically, the generalizability of using similarities as features is analyzed, design goals and methods for weighting… (More)

- Nathaniel P. Jacobson, Maya R. Gupta
- IEEE International Conference on Image Processing…
- 2005

Design goals and solutions are proposed for the display of hyperspectral imagery on tristimulus displays. The requirements of a hyperspectral visualization depend on the task. We focus on creating consistent representations of hyperspectral data that can facilitate understanding and analysis of hyperspectral scenes, and may be used in conjunction with… (More)

- Yihua Chen, Maya R. Gupta, Benjamin Recht
- ICML
- 2009

Similarity measures in many real applications generate indefinite similarity matrices. In this paper, we consider the problem of classification based on such indefinite similarities. These indefinite kernels can be problematic for standard kernel-based algorithms as the optimization problems become non-convex and the underlying theory is invalidated. In… (More)

- Raman Arora, Maya R. Gupta, Amol Kapila, Maryam Fazel
- ICML
- 2011

We propose clustering samples given their pairwise similarities by factorizing the similarity matrix into the product of a cluster probability matrix and its transpose. We propose a rotation-based algorithm to compute this left-stochastic decomposition (LSD). Theoretical results link the LSD clustering method to a soft kernel k-means clustering, give… (More)

- Maya R. Gupta, Yihua Chen
- Foundations and Trends in Signal Processing
- 2010

This introduction to the expectation–maximization (EM) algorithm provides an intuitive and mathematically rigorous understanding of EM. Two of the most popular applications of EM are described in detail: estimating Gaussian mixture models (GMMs), and estimating hidden Markov models (HMMs). EM solutions are also derived for learning an optimal mixture of… (More)

- Santosh Srivastava, Maya R. Gupta, Bela A. Frigyik
- Journal of Machine Learning Research
- 2007

Quadratic discriminant analysis is a common tool for classification, but estimation of the Gaussian parameters can be ill-posed. This paper contains theoretical and algorithmic contributions to Bayesian estimation for quadratic discriminant analysis. A distribution-based Bayesian classifier is derived using information geometry. Using a calculus of… (More)

- Nathan Parrish, Hyrum S. Anderson, Maya R. Gupta, Dun-Yu Hsiao
- Journal of Machine Learning Research
- 2013

We consider the problem of classifying a test sample given incomplete information. This problem arises naturally when data about a test sample is collected over time, or when costs must be incurred to compute the classification features. For example, in a distributed sensor network only a fraction of the sensors may have reported measurements at a certain… (More)

- Nathaniel P. Jacobson, Maya R. Gupta, Jeff B. Cole
- IEEE Trans. Geoscience and Remote Sensing
- 2007

Many remote-sensing applications produce large sets of images, such as hyperspectral images or time-indexed image sequences. We explore methods to display such image sets by linearly projecting them onto linear basis functions designed for the red, green, and blue primaries of a standard tristimulus display, for the human visual system, and for the SNR of… (More)

- Hyrum S Anderson, Maya R Gupta
- The Journal of the Acoustical Society of America
- 2008

This paper addresses the problem of classifying signals that have been corrupted by noise and unknown linear time-invariant (LTI) filtering such as multipath, given labeled uncorrupted training signals. A maximum a posteriori approach to the deconvolution and classification is considered, which produces estimates of the desired signal, the unknown channel,… (More)

- Luca Cazzanti, Maya R. Gupta, Anjali J. Koppal
- Pattern Recognition
- 2008

Generative Models for Similarity-based Classification