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- Christopher M. Bishop, Nasser M. Nasrabadi
- J. Electronic Imaging
- 2007

his book provides an introduction to the eld of pattern recognition and machine earning. It gives an overview of several asic and advanced topics in machine earning theory. The book is definitely aluable to scientists and engineers who re involved in developing machine learnng tools applied to signal and image proessing applications. This book is also… (More)

- Yi Chen, Nasser M. Nasrabadi, Trac D. Tran
- IEEE Trans. Geoscience and Remote Sensing
- 2011

A new sparsity-based algorithm for the classification of hyperspectral imagery is proposed in this paper. The proposed algorithm relies on the observation that a hyperspectral pixel can be sparsely represented by a linear combination of a few training samples from a structured dictionary. The sparse representation of an unknown pixel is expressed as a… (More)

- Yi Chen, Nasser M. Nasrabadi, Trac D. Tran
- IEEE Journal of Selected Topics in Signal…
- 2011

In this paper, we propose a new sparsity-based algorithm for automatic target detection in hyperspectral imagery (HSI). This algorithm is based on the concept that a pixel in HSI lies in a low-dimensional subspace and thus can be represented as a sparse linear combination of the training samples. The sparse representation (a sparse vector corresponding to… (More)

- Hien Van Nguyen, Vishal M. Patel, Nasser M. Nasrabadi, Rama Chellappa
- IEEE Transactions on Image Processing
- 2013

In this paper, we present dictionary learning methods for sparse signal representations in a high dimensional feature space. Using the kernel method, we describe how the well known dictionary learning approaches, such as the method of optimal directions and KSVD, can be made nonlinear. We analyze their kernel constructions and demonstrate their… (More)

- Yi Chen, Nasser M. Nasrabadi, Trac D. Tran
- 2011 18th IEEE International Conference on Image…
- 2011

In this paper, a new technique for hyperspectral image classification is proposed. Our approach relies on the sparse representation of a test sample with respect to all training samples in a feature space induced by a kernel function. Projecting the samples into the feature space and kernelizing the sparse representation improves the separability of the… (More)

- Heesung Kwon, Nasser M. Nasrabadi
- IEEE Trans. Geoscience and Remote Sensing
- 2005

- Nasser M. Nasrabadi, Robert A. King
- IEEE Trans. Communications
- 1988

- Jae-Soo Kim, Sahng H. Park, Patrick W. Dowd, Nasser M. Nasrabadi
- Wireless Personal Communications
- 1996

The channel assignment problem has become increasingly important in mobile telephone communication. Since the usable range of the frequency spectrum is limited, the optimal assignment problem of channels has become increasingly important. Recently Genetic Algorithms (GAs) have been proposed as new computational tools for solving optimization problems. GAs… (More)

- Nam H. Nguyen, Nasser M. Nasrabadi, Trac D. Tran
- IEEE Transactions on Information Theory
- 2011

This paper studies the problem of accurately recovering a <formula formulatype="inline"><tex Notation="TeX">$k$</tex> </formula>-sparse vector <formula formulatype="inline"><tex Notation="TeX">$\beta^{\star}\in\BBR^{p}$</tex> </formula> from highly corrupted linear measurements <formula formulatype="inline"><tex… (More)

- Sumit Shekhar, Vishal M. Patel, Nasser M. Nasrabadi, Rama Chellappa
- IEEE Transactions on Pattern Analysis and Machine…
- 2014

Traditional biometric recognition systems rely on a single biometric signature for authentication. While the advantage of using multiple sources of information for establishing the identity has been widely recognized, computational models for multimodal biometrics recognition have only recently received attention. We propose a multimodal sparse… (More)