N. Nasrabadi

Publications368
h-index45
Citations18,629
Highly Influential Citations1,024
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Pattern Recognition and Machine Learning
TLDR
Probability Distributions.- Linear models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models. Expand
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Hyperspectral Image Classification Using Dictionary-Based Sparse Representation
TLDR
A new sparsity-based algorithm for the classification of hyperspectral imagery is proposed in this paper. Expand
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Sparse Representation for Target Detection in Hyperspectral Imagery
TLDR
In this paper, we propose a new sparsity-based algorithm for automatic target detection in hyperspectral imagery (HSI). Expand
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Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery
  • H. Kwon, N. Nasrabadi
  • Mathematics, Computer Science
  • IEEE Transactions on Geoscience and Remote…
  • 24 January 2005
TLDR
We present a nonlinear version of the well-known anomaly detection method referred to as the RX-algorithm. Expand
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Hyperspectral Image Classification via Kernel Sparse Representation
TLDR
In this paper, a novel nonlinear technique for hyperspectral image (HSI) classification relies on sparsely representing a test sample in terms of all of the training samples in a feature space induced by a kernel function. Expand
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Joint Sparse Representation for Robust Multimodal Biometrics Recognition
TLDR
We propose a multimodal sparse representation method, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations. Expand
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Design of Non-Linear Kernel Dictionaries for Object Recognition
TLDR
In this paper, we present dictionary learning methods for sparse signal representations in a high dimensional feature space that exploit sparsity of data in a non-linear feature space. Expand
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Multi-View Automatic Target Recognition using Joint Sparse Representation
TLDR
We introduce a novel joint sparse representation based multiview automatic target recognition (ATR) method, which can not only handle multi-view ATR without knowing the pose but also has the advantage of exploiting the correlations among the multiple views of the same physical target. Expand
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Image coding using vector quantization: a review
TLDR
A review of vector quantization techniques used for encoding digital images is presented. Expand
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Kernel matched subspace detectors for hyperspectral target detection
  • H. Kwon, N. Nasrabadi
  • Mathematics, Computer Science
  • IEEE Transactions on Pattern Analysis and Machine…
  • 1 February 2006
TLDR
In this paper, we present a kernel realization of a matched subspace detector (MSD) that is based on a subspace mixture model defined in a high-dimensional feature space associated with a kernel function. Expand
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