Victor Paúl Pauca

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In this paper we discuss the development and use of low-rank approximate nonnegative matrix factorization (NMF) algorithms for feature extraction and identification in the fields of text mining and spectral data analysis. The evolution and convergence properties of hybrid methods based on both sparsity and smoothness constraints for the resulting(More)
Amethodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank nonnegative matrix factorization algorithm to retain natural data nonnegativity, thereby eliminating the need to use subtractive basis vector and encoding calculations present in(More)
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 the analysis process. Here we are concerned with the application of analyzing data obtained using astronomical spectrometers, which provide spectral data which is(More)
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. While ground-based telescopes with adaptive optics technology have been able to produce high-resolution images for a variety of spaced-based objects, current(More)
The improvement in optical image quality is now generally attempted in two stages. The rst stage involves techniques in adaptive-optics and occurs as the observed image is initially formed. The second stage of enhancing the quality of optical images generally occurs oo-line, and consists of the postprocessing step of image restoration. Image restoration is(More)
Automated iris recognition is a promising method for noninvasive verification of identity. Although it is noninvasive, the procedure requires considerable cooperation from the user. In typical acquisition systems, the subject must carefully position the head laterally to make sure that the captured iris falls within the field-of-view of the digital image(More)
We consider the problem of matching highly non-ideal ocular images where the iris information cannot be reliably used. Such images are characterized by non-uniform illumination, motion and de-focus blur, off-axis gaze, and non-linear deformations. To handle these variations, a single feature extraction and matching scheme is not sufficient. Therefore, we(More)
Socially relevant computing has recently been proposed as a way to reinvigorate interest in computer science. By appealing to students' interest in helping others, socially relevant computing aims to give students life-changing experiential learning not typically achieved in the classroom, while providing software that benefits society at large. For the(More)
The iris is a popular biometric that has been demonstrated to exhibit high matching accuracy and permanence under appropriate conditions. However, there are several limiting factors that are yet to be comprehensively addressed. One major drawback, in standard limited-focus iris recognition systems, is the restrictions imposed by the optical parameters of(More)