Pablo Rivas

Pablo   Rivas   Perea, Ph.D.

Adjunct Professor | Post-Doc
Department of Computer Science
Baylor University
Pablo_Rivas_Perea at Baylor.edu
Office Phone: +1 (254) 710-6965
Rogers ECS Building, Room 220.14

Research

My research is in machine learning, data science, deep learning, big data analytics, and large-scale data mining with applications to large-scale multidimensional multispectral signal analysis, statistical pattern recognition methods, image restoration, image analysis, intelligent software systems, and health-care imaging.

Here is a link to my Large-Scale Multispectral Multidimensional Analysis Laboratory Blog: www.lsmmalab.com

Our research on the detection of Retinoblastoma by searching for Leukocoria can be followed here: www.leuko.net

Other areas that have my attention include applied mathematics, numerical optimization, swarm intelligence optimization, evolutionary algorithms, soft computing, fuzzy logic, neural networks, and neurofuzzy systems.

Here is my curriculum vitae, here is my Academia.edu profile, and here is my Ph.D. dissertation.

Teaching

Schedule

Publications

Selected Recent Journal Articles

Pablo Rivas-Perea, Ryan Henning, Bryan Shaw, and Greg Hamerly, “Finding the Smallest Circle Containing the Iris in the Denoised Wavelet Domain”, in proceedings of the Image Analysis and Interpretation (SSIAI), 2014 IEEE Southwest Symposium on, pp. 13-16, 4/2014. - download -
Pablo Rivas-Perea, Juan Cota-Ruiz, Jose-Gerardo Rosiles, “Statistical and Neural Pattern Recognition Methods for Dust Aerosol Detection,” in International Journal of Remote Sensing, accepted for publication, 4/2013 - download -
Juan Cota-Ruiz, Jose-Gerardo Rosiles, Pablo Rivas-Perea, Ernesto Sifuentes,“A distributed localization algorithm for wireless sensor networks based on the solutions of spatially-constrained local problems”, in Sensors Journal, IEEE, 4/2013 - download -
Pablo Rivas-Perea, Juan Cota-Ruiz, Jose-Gerardo Rosiles, “An algorithm for training a large scale support vector machine for regression based on linear programming and decomposition methods”, in Pattern Recognition Letters, vol. 34, no. 4, pp. 439-451, 3/2013 - download -
Pablo Rivas-Perea, Juan Cota-Ruiz, Jose-Gerardo Rosiles, “A nonlinear least squares quasi-Newton strategy for LP-SVR hyper-parameters selection”, in International Journal of Machine Learning and Cybernetics, 2/2013 - download -
Pablo Rivas-Perea, Juan Cota-Ruiz, David Garcia Chaparro, J. A. Perez Venzor, Abel Quezada Carreon, Jose-Gerardo Rosiles, “Support Vector Machines for Regression: A Succinct Review of Large-Scale and Linear Programming Formulations”, in International Journal of Intelligence Science, vol. 3, pp. 5-14, 1/2013. - download -
Juan Cota-Ruiz, Jose-Gerardo Rosiles, Ernesto Sifuentes, Pablo Rivas-Perea, “A Low-Complexity Geometric Bilateration Method for Localization in Wireless Sensor Networks and Its Comparison with Least-Squares Methods”, in Sensors, vol. 12, pp. 839-862, 1/2012 - download -
Mario Ignacio Chacon Murguia, Yearim Quezada-Holguin, Pablo Rivas-Perea, Sergio Cabrera, “Dust Storm Detection Using a Neural Network with Uncertainty and Ambiguity Output Analysis”, in Pattern Recognition, ed. Jose Francisco Martinez-Trinidad et al., vol. 6718, Lecture Notes in Computer Science, Springer Berlin Heidelberg, pp. 305-313. 6/2011. - download -
Pablo Rivas-Perea, Jose G. Rosiles and Wei Qian, “Subjective Colocalization Analysis with Fuzzy Predicates,” in Soft Computing for Intelligent Control and Mobile Robotics, Oscar Castillo, Witold Pedrycz, Janusz Kacprzyk Eds. Computational Intelligence Series of Springer-Verlag. 1/2011. - download -

For a full list of publications see my CV. You can also see my Academia.edu or my Google Scholar profiles.

Ph.D. Dissertation

Abstract

The main contribution of this dissertation is the development of a method to train a Support Vector Regression (SVR) model for the large-scale case where the number of training samples supersedes the computational resources. The proposed scheme consists of posing the SVR problem entirely as a Linear Programming (LP) problem and on the development of a sequential optimization method based on variables decomposition, constraints decomposition, and the use of primal-dual interior point methods. Experimental results demonstrate that the proposed approach has comparable performance with other SV-based classifiers. Particularly, experiments demonstrate that as the problem size increases, the sparser the solution becomes, and more computational efficiency can be gained in comparison with other methods. To reduce the LP-SVR training time, a method is developed that takes advantage of the fact that the support vectors (SVs) are likely to lie on the convex hull of each class. The algorithm uses the Mahalanobis distance from the class sample mean in order to rank each sample in the training set; then the samples with the largest distances are used as part of the initial working set. Experimental results show a reduction in the total training time as well as a significant decrease in the total iterations percentage. Results also suggest that using the speedup strategy, the SVs are found earlier in the learning process. Also, this research introduces a method to find the set of LP-SVR hyper-parameters; experimental results show that the algorithm provides hyper-parameters that minimize an estimate of the true test generalization error. Finally, the SVR scheme shows state-of-the-art performance in various applications such as power load prediction forecasting, texture-based image segmentation, and classification of remotely sensed imagery. This demonstrates that the proposed learning scheme and the LP-SVR model are robust and efficient when compared with other methodologies for large-scale problems.

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Partial list of Funded Projects

Multispectral signature detection with large-scale machine learning methods, Consejo Nacional de Ciencia y Tenologia (mexican NSF), $15,000, 8/2013
Accelerating training of a large-scale SVM, Consejo Nacional de Ciencia y Tenologia (mexican NSF), $10,000, 8/2013
Statistical and Neural Pattern Recognition Methods for Dust Aerosol Detection, Consejo Nacional de Ciencia y Tenologia (mexican NSF), $18,000, 3/2012
Forecasting The Demand of Short-Term Electric Power Load with Large-Scale LP-SVR, Consejo Nacional de Ciencia y Tenologia (mexican NSF), $6,000, 3/2012
Numerical optimization strategies for LP-SVR hyper-parameters selection, Consejo Nacional de Ciencia y Tenologia (mexican NSF), $2,000, 3/2012
Decomposition Methods for Linear Programming Support Vector Regression in Large Scale Problems
- - Texas Instruments, TIF-UTEP, $2,800, 8/2010
- - Consejo Nacional de Ciencia y Tenologia (mexican NSF), Conacyt-UTEP, $33,992, 8/2008
Applications of Accurate Singular Value Decomposition vs Traditional SVD, SEP-DGRI, $1,260, 9/2010
Image Analysis in Multichannel Images: Colocalization Trough Fluorescent Microscopy, IEEE (IJCNN travel grant), $500, 6/2009
Southwestern U.S. and Northwestern Mexico Dust Storm Analysis Trough Remote Sensing Imagery, NASA Goddard Space Flight Center – University of Maryland Baltimore County, NASA-GEST-UTEP, $9,600, 5/2009
Face Recognition Under Non-Cooperative Environments Using Real Time Hough-KLT, ITCH-DGIT, $17,485.20, 9/2006
Mobile Face Recognition System for Non-Cooperative Environments, IEEE (student enterprise award), $1,500, 1/2007
Watch-Bot Mobile Surveillance System for Biometric Identification, ITCH-DGIT, $300, 9/2006
Development and Implementation of Digital Image Processing Algorithms for a Simulation and Code Generation Toolbox, $400, ITCH-DGIT, 5/2005
Fast Development of Real Time Digital Image Processing Applications, ITCH-DGIT, $400, 10/2005
In Motion Face Recognition Trough Multilayer Perceptrons, ITN-DGIT, $1,100, 5/2004
Slimmer, a Security Mobile Agent for User Authentication on 802.11 WLAN Environments, ITN-DGIT, $300, 6/2003
Syntactic Semantic Prevalidator System for Customs Declaration, ITN-DGIT, $200, 5/2002

See the complete list of funded projects in my curriculum vitae.

Professional Affiliations

I am a member of...

The Association for Computing Machinery (ACM), 2013–Present
Optical Society of America (OSA), 2010–Present
HKN (Eta Kappa Nu), the electrical and computer engineering honor society of the IEEE, Life-long Member since 2011
The Hispanic-American Fuzzy Systems Association, 2006–Present
The International Association of Engineers (IAENG), 2006–Present
The Society for Industrial and Applied Mathematics (SIAM), 2004–Present
IEEE Computational Intelligence Society, 2003–Present
IEEE Computer Society, 2002–Present
The Institute of Electrical and Electronics Engineers (IEEE), 2001–Present

Short Biography