# Guangliang Chen

Associate Professor

San Jose State University

Department of Mathematics and Statistics

San Jose, CA 95192-0103

Office: 417 MacQuarrie Hall

Phone: (408) 924-5131

Fax: 408-924-5080

Email: firstname.lastname@sjsu.edu

Curriculum Vitae | Teaching | Research

## Education

- Ph.D. Applied Math, University of Minnesota, 2009
- B.S. Math, University of Science & Technology of China, 2003

## Teaching

#### Spring 2023

- Math 180R: Undergraduate Research in Mathematics (1)
- Math 250: Mathematical Data Visualization [Syllabus]
- Math 298: Special Study (1)

#### Fall 2022

- Math 39: Linear Algebra I [Syllabus]
- Math 251: Statistical and Machine Learning Classification [Syllabus]
- Math 297A: Preparation for Writing Project (1)
- Math 298: Special Study (1)

#### Spring 2022

- Math 185: Learning from Large Data [Syllabus]
- Math 250: Mathematical Data Visualization [Syllabus]
- Math 297A: Preparation for Writing Project (1)

#### Summer 2021

#### Spring 2021

- Math 250: Mathematical Methods for Data Visualization [Syllabus]
- Math 263: Stochastic Processes [Syllabus]

#### Fall 2020

- Math 251: Statistical and Machine Learning Classification [Syllabus]
- Math 261A: Regression Theory and Methods [Syllabus]

#### Summer 2020

- Math 161A: Applied Probability and Statistics I [Syllabus]

#### Spring 2020

- Math 161A: Applied Probability and Statistics I [Syllabus]
- Math 253: Mathematical Methods for Data Visualization [Syllabus]
- Math 298: Special Study (1)

#### Fall 2019

- Math 261A: Regression Theory and Methods [Syllabus]
- Math 203: Applied Mathematics, Computing & Statistics Projects (CAMCOS)
- Math 297A: Preparation for Writing Project (1)

#### Spring 2019

- Math 161A: Applied Probability and Statistics I [Syllabus]
- Math 263: Stochastic Processes [Syllabus]

#### Fall 2018

- Math 129A: Linear Algebra I [Syllabus]
- Math 251: Statistical and Machine Learning Classification [Syllabus]
- Math 297A: Preparation for Writing Project (1)

#### Spring 2018

- Math 161A: Applied Probability and Statistics [Syllabus]
- Math 203: Applied Mathematics, Computing & Statistics Projects (CAMCOS) [Final presentation] [Report]

#### Fall 2017

- Math 163: Probability Theory [Syllabus]

#### Spring 2017

- Math 203: CAMCOS [Final presentation] [Report]
- Math 261B: Design and Analysis of Experiments [Syllabus]

#### Fall 2016

#### Spring 2016

- Math 285: Classification with Handwritten Digits [Syllabus]
- Math 298: Special Study (1)

#### Fall 2015

- Math 203: CAMCOS [Group 1 presentation] [Group 2 presentation] [Combined report]
- Math 285: Selected Topics in High Dimensional Data Modeling [Syllabus]

## Research

My research interests include subspace/manifold clustering, dictionary clustering, and classification, as well as applications to documents and image processing.#### Refereed Journal Papers

- A General Framework for Scalable Spectral Clustering Based on Document Models. G. Chen. Pattern Recognition Letters, 125: 488-493, July 2019
- High Dimensional Data Modeling Techniques for Detection of Chemical Plumes and Anomalies in Hyperspectral Images and Movies. Y. Wang, G. Chen, and M. Maggioni. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Volume: PP, Issue: 99, Pages 1-9, 2016.
- Compressive Sensing and Dictionary Learning. G. Chen and D. Needell. Finite Frame Theory, Proceedings of Symposia in Applied Mathematics, vol. 73, Amer. Math. Soc., Providence, RI, pp. 201-241, 2016.
- Guaranteed Sparse Signal Recovery with Highly Coherent Sensing Matrices. G. Chen, A. Divekar, and D. Needell. SampTA Special Issue of Sampling Theory in Signal and Image Processing, 13(1): 91-109, 2014.
- Multiscale Geometric Methods for Data Sets II: Geometric Multi-Resolution Analysis. W.K. Allard, G. Chen and M. Maggioni. Applied and Computational Harmonic Analysis (ACHA), 32(3): 435-462, 2012.
- Spectral Clustering based on Local Linear Approximations. E. Arias-Castro, G. Chen and G. Lerman. Electronic Journal of Statistics, 5: 1537-1587, 2011
- Spectral Curvature Clustering (SCC). G. Chen and G. Lerman. Int. J. Comput. Vis., 81: 317-330, 2009
- Foundations of a Multi-way Spectral Clustering Framework for Hybrid Linear Modeling. G. Chen and G. Lerman. Found. Comput. Math., 9: 517-558, 2009
- Functional Genomics via Multiscale Analysis: Application to Gene Expression and ChIP-on-chip Data. G. Lerman, J. McQuown, A. Blais, B. Dynlacht, G. Chen and B. Mishra. Bioinformatics, 23(3): 314-320, 2007

#### Conference Proceedings

- The Larger the Better: Analysis of a Scalable Spectral Clustering Algorithm with Cosine Similarity, G. Chen. In Proceedings of the 3rd International Conference on Machine Learning and Intelligent Systems (MLIS), November 2021
- Efficient, Geometrically Adaptive Techniques for Multiscale Gaussian-Kernel SVM Classification. G. Chen. In: Imaizumi, T., Okada, A., Miyamoto, S., Sakaori, F., Yamamoto, Y., Vichi, M. (eds) Advanced Studies in Classification and Data Science. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Singapore.
- MATLAB Implementation Details of a Scalable Spectral Clustering Algorithm with Cosine Similarity, G. Chen,
*The 2nd Workshop on Reproducible Research in Pattern Recognition (RRPR 2018),*Beijing, China, August 2018 - A Scalable Spectral Clustering Algorithm based on Landmark Embedding and Cosine Similarity, G. Chen,
*IAPR Joint International Workshops on Statistical Techniques in Pattern Recognition (SPR 2018) and Structural and Syntactic Pattern Recognition (SSPR 2018),*Fragrant Hill, Beijing, China, August 2018 - Scalable Spectral Clustering with Cosine Similarity, G. Chen,
*The 24**th**International Conference on Pattern Recognition (ICPR)*, Beijing, China, August 2018 - Large-scale Spectral Clustering using Diffusion Coordinates on Landmark-based Bipartite Graphs, K. Pham and G. Chen,
*The 12**th**Workshop on Graph-based Natural Language Processing (TextGraphs-12)*, New Orleans, Louisiana, June 2018 - Simple, Fast and Accurate Hyper-parameter Tuning in Gaussian-kernel SVM, G. Chen, W. Florero-Salinas, and D. Li, International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, May 2017
- A Nearest Neighbor Approach for Efficient Selection of the Bandwidth Parameter in Gaussian-kernel Support Vector Machines, W. Florero-Salinas, D. Li, and G. Chen, Pacific Conference on Statistical Computing and Data Mining, Palm Springs, CA, May 2016
- Enhanced Detection of Chemical Plumes in Hyperspectral Images and Movies through Improved Background Modeling, Y. Wang, M. Maggioni, and G. Chen, The 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), University of Tokyo, Japan, June 2015
- A Novel Multiscale Geometric Approach to Structured Dictionary Learning on High Dimensional Data, G. Chen, The 11th International Conference on Sampling Theory and Applications (SampTA), American University, Washington, DC, May 2015
- A Fast Multiscale Framework for Data in High-Dimensions: Measure Estimation, Anomaly Detection, and Compressive Measurements, G. Chen, M. Iwen, S. Chin, and M. Maggioni, Visual Communications and Image Processing (VCIP), San Diego, CA, November 2012
- Multiscale Geometric and Spectral Analysis of Plane Arrangements, G. Chen and M. Maggioni, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, June 2011
- Multiscale Geometric Dictionaries for Point-Cloud Data, G. Chen, and M. Maggioni, The 9th International Conference on Sampling Theory and Applications (SampTA), Singapore, May 2011
- Data Representation and Exploration with Geometric Wavelets, E. Monson, G. Chen, R. Brady, and M. Maggioni, IEEE Symposium on Visual Analytics Science and Technology (VAST), Salt Lake City, UT, 2010
- Multiscale Geometric Wavelets for the Analysis of Point Clouds, G. Chen and M. Maggioni, The 44th Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, 2010
- Kernel Spectral Curvature Clustering (KSCC), G. Chen, S. Atev and G. Lerman, The 4th ICCV International Workshop on Dynamical Vision, Kyoto, Japan, 2009 (Best paper award)
- Motion Segmentation for Hopkins155 Database via SCC, G. Chen and G. Lerman, The 4th ICCV International Workshop on Dynamical Vision, Kyoto, Japan, 2009

#### Book Chapters

- Multi-Resolution Geometric Analysis for Data in High Dimensions, G. Chen, A.V. Little, and M. Maggioni, in Excursions in Harmonic Analysis, Volume 1, Springer, New York, 2013
- Some Recent Advances in the Geometric Analysis of Point Clouds in High Dimensions, G. Chen, A.V. Little, M. Maggioni and L. Rosasco, in Wavelets and Multiscale Analysis: Theory and Applications, March 2011, Springer

#### Technical Report

- Multiscale Analysis for Muon-Scattering Data, G. Chen, G. Lerman and R. Chartrand, Technical Report LA-UR 06-7504 (2006), Los Alamos National Laboratory

#### PhD Thesis

- Spectral Curvature Clustering for Hybrid Linear Modeling, Guangliang Chen, University of Minnesota, July 2009

## Past projects

- Multiscale Analysis of Plane Arrangements (MAPA)
- Geometric MultiResolution Analysis (GMRA)
- Higher Order Spectral Clustering (HOSC)
- Kernel Spectral Curvature Clustering (KSCC)
- Spectral Curvature Clustering (SCC)

## Links

- ICSA 2023: [Lecture slides] [Matlab scripts]

Created on 11/13/2014; Last updated on 3/22/2023