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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)

- Maya R. Gupta, Nathaniel P. Jacobson, Eric K. Garcia
- Pattern Recognition
- 2007

We consider the problem of document binarization as a pre-processing step for optical character recognition (OCR) for the purpose of keyword search of historical printed documents. A number of promising techniques from the literature for binarization, pre-filtering, and post-binarization denoising were implemented along with newly developed methods for… (More)

- Maya R. Gupta, Eric K. Garcia, E. Chin
- IEEE Transactions on Image Processing
- 2008

Local learning methods, such as local linear regression and nearest neighbor classifiers, base estimates on nearby training samples, neighbors. Usually, the number of neighbors used in estimation is fixed to be a global ldquooptimalrdquo value, chosen by cross validation. This paper proposes adapting the number of neighbors used for estimation to the local… (More)

- Eric K. Garcia, Sergey Feldman, Maya R. Gupta, Santosh Srivastava
- IEEE Transactions on Knowledge and Data…
- 2010

Local classifiers are sometimes called lazy learners because they do not train a classifier until presented with a test sample. However, such methods are generally not completely lazy because the neighborhood size k (or other locality parameter) is usually chosen by cross validation on the training set, which can require significant preprocessing and risks… (More)

- Eric K. Garcia, Raman Arora, Maya R. Gupta
- IEEE Transactions on Image Processing
- 2012

In many applications of regression, one is concerned with the efficiency of the estimated function in addition to the accuracy of the regression. For efficiency, it is common to represent the estimated function as a rectangular lattice of values—a lookup table (LUT)—that can be linearly interpolated for any needed value. Typically, a LUT is… (More)

- Eric K. Garcia, Maya R. Gupta
- NIPS
- 2009

We present a new empirical risk minimization framework for approximating functions from training samples for low-dimensional regression applications where a lattice (look-up table) is stored and interpolated at run-time for an efficient implementation. Rather than evaluating a fitted function at the lattice nodes without regard to the fact that samples will… (More)

- Erika M. Chin, Eric K. Garcia, Maya R. Gupta
- 2007 IEEE International Conference on Image…
- 2007

A popular color management standard for controlling color reproduction is the ICC color profile. The core of the ICC profile is a look-up-table which maps a regular grid of device-independent colors to the printer colorspace. To estimate the look-up-table from sample input-output colors, local linear regression has been shown to work better than other… (More)

- Eric K. Garcia, Maya R. Gupta
- Color Imaging Conference
- 2010

We focus on a recently proposed regression framework termed lattice regression, as applied to the construction of multidimensional color management look-up tables from empirical measurements. The key idea of lattice regression is that the construction of a look-up table should take into account the interpolation function used in its final implementation.… (More)

- Maya R. Gupta, Eric K. Garcia, Andrey Stroilov
- J. Electronic Imaging
- 2008

Custom color transformation – Learning from a small set of sample color pairs – Using the local ridge regression over proposed enclosing neighborhoods • Including a variant of Sibson’s natural neighbors – Evaluation • Fidelity and the presence of objectionable artifacts in enhanced images

- Eric K. Garcia, Maya R. Gupta
- Color Imaging Conference
- 2009

A system-optimized framework is presented for learning a multi-dimensional look-up-table (LUT) from training samples. The technique, termed lattice regression, solves for an entire LUT at once by optimizing the three-fold objective of 1) low interpolation error on training data, 2) smooth transitions between adjacent LUT outputs, and 3) a steady overall… (More)

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