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Algorithms and applications for approximate nonnegative matrix factorization
The development and use of low-rank approximate nonnegative matrix factorization algorithms for feature extraction and identification in the fields of text mining and spectral data analysis and the interpretability of NMF outputs in specific contexts are provided. Expand
Nonnegative matrix factorization for spectral data analysis
Data analysis is pervasive throughout business, engineering and science. Very often the data to be analyzed is nonnegative, and it is often preferable to take this constraint into account in theExpand
Document clustering using nonnegative matrix factorization
A methodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection using a low rank nonnegative matrix factorization algorithm to retain natural data nonnegativity, thereby eliminating the need for subtractive basis vector and encoding calculations present in other techniques. Expand
Object Characterization from Spectral Data Using Nonnegative Factorization and Information Theory
The identification and classification of non-imaging space objects, and ultimately the determination of their shape, function, and status, is an important but difficult problem still to be resolved.Expand
Matching highly non-ideal ocular images: An information fusion approach
Experiments on the extremely challenging Face and Ocular Challenge Series (FOCS) database and a subset of the Face Recognition Grand Challenge (FRGC) database confirm the efficacy of the proposed approach to perform ocular recognition. Expand
Iris Segmentation for Challenging Periocular Images
This chapter discusses the performance of five different iris segmentation algorithms on challenging periocular images. The goal is to convey some of the difficulties in localizing the iris structureExpand
Engineering the pupil phase to improve image quality
This work proposes tailoring the pupil phase profile by minimizing the sensitivity of the quality of the phase-encoded image of a point source to both its lateral and longitudinal coordinates using the Strehl ratio as a measure of image quality. Expand
Mobile apps for the greater good: a socially relevant approach to software engineering
The Wake Forest University Computer Science Department has been using mobile device programming, agile methods, and real-world, socially relevant projects for teaching software engineering in a liberal arts Computer Science curricula to give students life-changing experiential learning not typically achieved in the classroom. Expand
Classification of pixel-level fused hyperspectral and lidar data using deep convolutional neural networks
This work investigates classification from pixel-level fusion of Hyperspectral (HSI) and Light Detection and Ranging (LiDAR) data using convolutional neural networks (CNN), and addresses key questions relative to classification performance. Expand