Doctor Yi Guo

Doctor Yi Guo

Senior Lecturer In Data Science,
Mathematics

Senior Lecturer In Data Science,
Mathematics

Senior Lecturer In Data Science,
Mathematics

Biography

Yi Guo received the B. Eng. (Hons.) in instrumenta- tion from the North China University of Technology in 1998, and the M. Eng. in automatic control from Central South University in 2002. From 2005, he studied Computer Science at the University of New England, Armidale, Australia, focusing on di- mensionality reduction for structured data with no vectorial representation. He received a Ph.D. degree in 2008. Between 2008 and 2016, he was with CSIRO, working as a computational statistician on various projects in spectroscopy, remote sensing and materials science. He recently joined Center for Research in Mathematics at the School of Computing, Engineering and Mathematics, Western Sydney University. 

This information has been contributed by Doctor Guo.

Qualifications

  • PhD University of New England

Professional Memberships

  • IEEE member (2016 - 2017)

Organisational Unit (School / Division)

  • Mathematics
  • Mathematics
  • Mathematics

Contact

Email: Y.Guo@westernsydney.edu.au
Phone: (02) 9685 9374
Mobile:
Location: EN.1.35
Parramatta
Website:

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Teaching

Previous Teaching Areas

  • 301107 Analytics Programming, 2017
  • 301110 Applications of Big Data, 2017
  • 301113 Programming for Data Science, 2017

Publications

Journal Articles

  • Yin, M., Gao, J., Xie, S. and Guo, Y. (2019), 'Multiview subspace clustering via tensorial t-product representation', IEEE Transactions on Neural Networks and Learning Systems, vol 30, no 3 , pp 852 - 864.
  • Liu, Y., Guo, Y., Li, F., Xin, L. and Huang, P. (2019), 'Sparse dictionary learning for blind hyperspectral unmixing', IEEE Geoscience and Remote Sensing Letters, vol 16, no 4 , pp 578 - 582.
  • Yang, J., Guo, Y., Yang, Z. and Xie, S. (2019), 'Under-determined convolutive blind source separation combining density-based clustering and sparse reconstruction in time-frequency domain', IEEE Transactions on Circuits and Systems I: Regular Papers, vol 66, no 8 , pp 3015 - 3027.
  • Traylen, A., Caccetta, P., Guo, Y., Berman, M. and Lau, I. (2018), 'Endmember search and proportion estimates from airborne hyperspectral surveys', International Journal of Remote Sensing, vol 39, no 2 , pp 525 - 543.
  • Li, F., Xin, L., Guo, Y., Gao, D., Kong, X. and Jia, X. (2018), 'Super-resolution for GaoFen-4 remote sensing images', IEEE Geoscience and Remote Sensing Letters, vol 15, no 1 , pp 28 - 32.
  • Berman, M., Hao, Z., Stone, G. and Guo, Y. (2018), 'An investigation into the impact of band error variance estimation on intrinsic dimension estimation in hyperspectral images', IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 11, no 9 , pp 3279 - 3296.
  • Li, F., Xin, L., Guo, Y., Gao, J. and Jia, X. (2017), 'A framework of mixed sparse representations for remote sensing images', IEEE Transactions on Geoscience and Remote Sensing, vol 55, no 2 , pp 1210 - 1221.
  • Guo, Y., Li, F., Caccetta, P. and Devereux, D. (2017), 'Multiple temporal mosaicing for Landsat satellite images', Journal of Applied Remote Sensing, vol 11, no 1 .
  • Berman, M., Bischof, L., Lagerstrom, R., Guo, Y., Huntington, J., Mason, P. and Green, A. (2017), 'A comparison between three sparse unmixing algorithms using a large library of shortwave infrared mineral spectra', IEEE Transactions on Geoscience and Remote Sensing, vol 55, no 6 , pp 3588 - 3610.
  • Gao, H., Zhang, Y., Wang, W., Zhao, K., Liu, C., Bai, L., Li, R. and Guo, Y. (2017), 'Two membrane-anchored aspartic proteases contribute to pollen and ovule development', Plant Physiology, vol 173, no 1 , pp 219 - 239.
  • Zhai, Y., Zhang, L., Wang, N., Guo, Y., Cen, Y., Wu, T. and Tong, Q. (2016), 'A modified locality-preserving projection approach for hyperspectral image classification', IEEE Geoscience and Remote Sensing Letters, vol 13, no 8 , pp 1059 - 1063.
  • Hao, Z., Berman, M., Guo, Y., Stone, G. and Johnstone, I. (2016), 'Semi-realistic simulations of natural hyperspectral scenes', IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 9, no 9 , pp 4407 - 4419.
  • Guo, Y., Gao, J. and Li, F. (2015), 'Random spatial subspace clustering', Knowledge-Based Systems, vol 74 , pp 106 - 118.
  • Sun, X., Zhang, L., Yang, H., Wu, T., Cen, Y. and Guo, Y. (2015), 'Enhancement of spectral resolution for remotely sensed multispectral image', IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 8, no 5 , pp 2198 - 2211.
  • O'Brien, A., Saunders, N., Guo, Y., Buske, F., Scott, R. and Bauer, D. (2015), 'VariantSpark : population scale clustering of genotype information', BMC Genomics, vol 16, no 1 .
  • Clifford, D. and Guo, Y. (2015), 'Combining two soil property rasters using an adaptive gating approach', Soil Research, vol 53, no 8 , pp 907 - 912.
  • Yin, M., Gao, J., Lin, Z., Shi, Q. and Guo, Y. (2015), 'Dual graph regularized latent low-rank representation for subspace clustering', IEEE Transactions on Image Processing, vol 24, no 12 , pp 4918 - 4933.
  • Guo, Y., Berman, M. and Gao, J. (2014), 'Group subset selection for linear regression', Computational Statistics & Data Analysis, vol 75 , pp 39 - 52.
  • Li, F., Li, C., Tang, L. and Guo, Y. (2014), 'Elastic registration for airborne multispectral line scanners', Journal of Applied Remote Sensing, vol 8, no 1 .
  • Guo, Y., Gao, J. and Li, F. (2014), 'Spatial subspace clustering for drill hole spectral data', Journal of Applied Remote Sensing, vol 8, no 1 .
  • Guo, Y. and Berman, M. (2012), 'A comparison between subset selection and L1 regularisation with an application in spectroscopy', Chemometrics and Intelligent Laboratory Systems, vol 118 , pp 127 - 138.
  • Kwan, P., Gao, J., Guo, Y. and Kameyama, K. (2010), 'A learning framework for adaptive fingerprint identification using relevance feedback', International Journal of Pattern Recognition and Artificial Intelligence, vol 24, no 1 , pp 15 - 38.
  • Gao, J., Kwan, P. and Guo, Y. (2009), 'Robust multivariate L1 principal component analysis and dimensionality reduction', Neurocomputing, vol 72, no 4-6 , pp 1242 - 1249.
  • Guo, Y., Gao, J. and Kwan, P. (2008), 'Twin kernel embedding', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 30, no 8 , pp 1490 - 1495.
  • Guo, Y., Gao, J., Kwan, P. and Hou, K. (2008), 'Visualization of protein structure relationships using constrained twin kernel embedding', Journal of Biomedical Science and Engineering, vol 1, no 2 , pp 133 - 140.

Conference Papers

  • Mazumdar, K., Zhang, D. and Guo, Y. (2019), 'Portfolio risk optimisation and diversification using swarm intelligence', Pacific Rim International Conference on Artificial Intelligence, Yanuca Island, Fiji.
  • Mazumdar, K., Zhang, D. and Guo, Y. (2019), 'Mulit-peak algorithmic trading strategies using Grey Wolf Optimizer', Pacific Rim International Conference on Artificial Intelligence, Yanuca Island, Fiji.
  • Liu, Y., Guo, Y., Li, F., Xin, L. and Huang, P. (2018), 'A fast algorithm to find all paths for hyperspectral unmixing', International Geoscience and Remote Sensing Symposium, Valencia, Spain.
  • Li, F., Xin, L., Guo, Y. and Jia, X. (2018), 'Multitemporal mid-infrared imagery based calibration and super resolution for Gaofen-4', International Geoscience and Remote Sensing Symposium, Valencia, Spain.
  • Guo, Y., Green, S., Park, L. and Rispen, L. (2018), 'Left ventricle volume measuring using echocardiography sequences', DICTA (Conference), Canberra, A.C.T..
  • Yang, J., Yang, Z., Guo, Y. and Xie, S. (2017), 'Blind source separation : detecting unknown sources number in covariance domain', International Conference on Computer and Automation Engineering, Sydney, N.S.W..
  • Ke, J., Guo, Y. and Sowmya, A. (2017), 'A fast approximate spectral unmixing algorithm based on segmentation', IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii.
  • Gao, J., Guo, Y. and Wang, Z. (2017), 'Matrix neural networks', International Symposium on Neural Networks, Sapporo, Japan.
  • Li, F., Xin, L., Liu, Y., Fu, J., Liu, Y. and Guo, Y. (2017), 'High efficient optical remote sensing images acquisition for nano-satellite-framework', Sensors, Systems, and Next-Generation Satellites (Conference), Warsaw, Poland.
  • Guo, Y., Li, F., Caccetta, P., Devereux, D. and Berman, M. (2016), 'Cloud filtering for Landsat TM satellite images using multiple temporal mosaicing', International Geoscience and Remote Sensing Symposium, Beijing, China.
  • Yin, M., Guo, Y., Gao, J., He, Z. and Xie, S. (2016), 'Kernel sparse subspace clustering on symmetric positive definite manifolds', IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, Nev..
  • Ke, J., Sowmya, A., Guo, Y., Bednarz, T. and Buckley, M. (2016), 'Efficient GPU computing framework of cloud filtering in remotely sensed image processing', DICTA (Conference), Gold Coast, Qld..
  • Li, F., Cornwell, T., De Hoog, F., Xin, L. and Guo, Y. (2016), 'Compressive sensing based multi-frequency synthesis', International Conference on Digital Signal Processing , Beijing, China.
  • Hao, Z., Berman, M., Guo, Y., Stone, G. and Johnstone, I. (2015), 'Semi-realistic simulations of natural hyperspectral scenes', IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy.
  • Tierney, S., Guo, Y. and Gao, J. (2015), 'Selective multi-source total variation image restoration', DICTA (Conference), Adelaide, S.A..
  • Guo, Y., Gao, J., Li, F., Tierney, S. and Yin, M. (2015), 'Low rank sequential subspace clustering', International Joint Conference on Neural Networks, Killarney, Ireland.
  • Tierney, S., Gao, J. and Guo, Y. (2014), 'Affinity pansharpening and image fusion', DICTA (Conference), Wollongong, N.S.W..
  • Tan, X., Sun, C., Wang, D., Guo, Y. and Pham, T. (2014), 'Soft cost aggregation with multi-resolution fusion', European Conference on Computer Vision, Zurich, Switzerland.
  • Tierney, S., Gao, J. and Guo, Y. (2014), 'The W-penalty and its application to alpha matting with sparse labels', DICTA (Conference), Wollongong, N.S.W..
  • Tierney, S., Gao, J. and Guo, Y. (2014), 'Subspace clustering for sequential data', IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Columbus, Ohio.
  • Sun, Y., Gao, J., Hong, X., Guo, Y. and Harris, C. (2014), 'Dimensionality reduction assisted tensor clustering', International Joint Conference on Neural Networks, Beijing, China.
  • Yin, M., Guo, Y. and Gao, J. (2014), 'Linear Subspace Learning via sparse dimension reduction', International Joint Conference on Neural Networks, Beijing, China.
  • Guo, Y., Gao, J. and Li, F. (2013), 'Spatial subspace clustering for hyperspectral data segmentation', International Conference on Digital Information Processing and Communications, Dubai, United Arab Emirates.
  • Hong, X., Guo, Y., Chen, S. and Gao, J. (2013), 'Sparse model construction using coordinate descent optimization', International Conference on Digital Signal Processing, Santorini, Greece.
  • Guo, Y., Gao, J. and Li, F. (2013), 'Large scale hyperspectral data segmentation by random spatial subspace clustering', International Geoscience and Remote Sensing Symposium, Melbourne, Vic..
  • Gao, J., Guo, Y. and Yin, M. (2013), 'Restricted Boltzmann machine approach to couple dictionary training for image super-resolution', International Conference on Image Processing , Melbourne, Vic..
  • Guo, Y., Gao, J. and Sun, Y. (2013), 'Endmember extraction by exemplar finder', ADMA (Conference), Hangzhou, China.
  • Guo, Y., Gao, J. and Li, F. (2013), 'Dimensionality reduction with dimension selection', Pacific-Asia Conference on Knowledge Discovery and Data Mining, Gold Coast, Qld..
  • Li, F., Tang, L., Li, C., Guo, Y. and Gao, J. (2013), 'A new super resolution method based on combined sparse representations for remote sensing imagery', Image and Signal Processing for Remote Sensing, Dresden, Germany.
  • Guo, Y., Gao, J. and Hong, X. (2012), 'Constrained grouped sparsity', Australasian Joint Conference on Artificial Intelligence, Sydney, N.S.W..
  • Li, F., Li, C., Tang, L. and Guo, Y. (2012), 'Elastic band-to-band registration for airborne multispectral scanners with large field of view', Image and Signal Processing for Remote Sensing, Edinburgh, U.K..
  • Guo, Y. and Gao, J. (2011), 'Local feature based tensor kernel for image manifold learning', Pacific-Asia Conference on Knowledge Discovery and Data Mining, Shenzhen, China.
  • Guo, Y., Gao, J. and Kwan, P. (2009), 'Regularized Kernel Local Linear Embedding on dimensionality reduction for non-vectorial data', Australasian Joint Conference on Artificial Intelligence, Melbourne, Vic..

Other Publications

  • 2011, 'An Unmixing Algorithm Based on a Large Library of Shortwave Infrared Spectra', Research Report

Dr. Yi Guo's research interests are in machine learning, computational statistics and applied mathematics such as optimisation, all with strong application focus in the areas such as environment monitoring, material science, medical science and so on.

 The models he worked on include

  • Dimensionality reduction
  • Manifold learning
  • Robust models
  • Blind source separation
  • Subspace clustering

 These methodologies have been applied to many problems in computer vision, image processing, pattern recognition and so on.

 His recent research focuses on data science including 

  • spatial temporal models (for multiway correlated data) 
  • complex neural networks 
  • neural processing

This information has been contributed by Doctor Guo.

Media

Title: Personal website
Description: My personal website

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