1. Outline Introductions My research and contributions Additional information References
Summary of research activities
Tianpei Xie,
Advisor: Alfred O. Hero
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2. Outline Introductions My research and contributions Additional information References
1 Introductions
Backgrounds
Robust learning from multiple sources
2 My research and contributions
3 Additional information
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3. Outline Introductions My research and contributions Additional information References
Backgrounds
• With the advent of Big Data era, we experienced a great explosion in terms of
1 the amount of data that is public available;
2 the diversity of multiple data sources that are accessible simultaneously;
3 the power of computational resource.
Figure : One of the central neighborhood in Twitter
Network. https://dhs.standford.edu/
gephi-workshop/twitter-network-gallery/
Figure : Multi-modality data source
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4. Outline Introductions My research and contributions Additional information References
Beyond the conventional machine learning
New challenges in machine learning area:
• Robustness of model in terms of low quality training data;
• Learning from multiple information sources;
• Ability in handling data inconsistency and high dimensionality.
• Interest: single-source learning with clean training set ⇒ robust
learning from multiple sources using information theory.
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5. Outline Introductions My research and contributions Additional information References
Previous work
Robust learning
• Robust learning via surrogate loss e.g. [Bartlett and Mendelson,
2003], [Bousquet and Elisseeff, 2002], [Tyler, 2008], ROD [Xu et al.,
2006].
• Anomaly detection e.g. SVDD [Sch¨olkopf et al., 1999], GEM [Hero,
2006].
• Cons: sensitive to outlier in training sample, and solves a non-convex
optimization.
Learning from multiple source (Multi-view learning)
• Co-regularization on Euclidean feature space e.g. CCA [Hardoon
et al., 2004], SVM-2K [Farquhar et al., 2005], Neural Nets [Ngiam et al.,
2011]
• Cons: lack of ability to handle data with high uncertainty, high
dimensionality and between-view inconsistency.
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6. Outline Introductions My research and contributions Additional information References
Contributions
Robust learning:
• Proposed the GEM-MED algorithm [Xie et al., 2014] as a joint classification +
anomaly detection on noisy training set.
• Rely on the GEM estimator [Hero, 2006], a non-parametric entropy estimator.
(a)
0 0.2 0.4 0.6 0.8 1
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precision
ROD−0.02
ROD−0.1
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ROD−0.6
GEM−MED
(b)
Figure : (a) The low-entropy region estimated by GEM [Hero, 2006] to separate outlier (red triangle) from the
nominal (circle and square) (b) The Precision-Recall curve for anomaly detection under given corruption rate
for GEM-MED and ROD.
• It only needs to solve a convex problem.
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7. Outline Introductions My research and contributions Additional information References
Our Contributions
Multi-view learning on statistical manifold:
• Assume data is given by parametric probability density function (p.d.f.) (data
with uncertainty.) and lies in a statistical manifold (space of all parametric p.d.f.).
• Proposed the CMV-MED algorithm [Xie et al., 2015] as the Co-regularization on
Statistical manifold, i.e. learning multiple models from the p.d.f. data.
• A robust consensus measure to quantify the between-view inconsistency using
the information divergence between p.d.fs .
2
classifier 1
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classifier 2
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distance
(a)
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Figure : (a) The proposed stochastic consensus constraint on statistical manifold as a robust inconsistency
measure. (b) The interpretation of GEM-MED as averaging multiple statistical models on the manifold.
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8. Outline Introductions My research and contributions Additional information References
Current research: Node prediction in network
• Learning to predict node attributes by combining the network graph
topology and node distribution
... ... ...
? ?
personal info. friendship
node attribute
(meta data)
edge structure
ID
⇔
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9. Outline Introductions My research and contributions Additional information References
List of publications
List of relevant publications:
1 Xie, Tianpei, Nasser M. Nasrabadi, and Alfred O. Hero. ”Semi-supervised Multi-view learning
on statistical manifold via stochastic consensus constraints.” in preparation.
2 Xie, Tianpei, Nasser M. Nasrabadi, and Alfred O. Hero. ”Learning to classify with possible
sensor failures.” submitted to IEEE Transaction on Signal Processing, 2016
3 Xie, Tianpei, Nasser M. Nasrabadi, and Alfred O. Hero. ”Semi-supervised multi-sensor
classification via consensus-based Multi-View Maximum Entropy Discrimination.” In
Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on,
pp. 1936-1940. IEEE, 2015.
4 Xie, Tianpei, Nasser M. Nasrabadi, and Alfred O. Hero. ”Learning to classify with possible
sensor failures.” In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE
International Conference on, pp. 2395-2399. IEEE, 2014.
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10. Outline Introductions My research and contributions Additional information References
Websites and detailed information
• Contact:
Tianpei (Luke) Xie
Department of Electrical and Computing Engineering, University of
Michigan, Ann Arbor,
Office: 4313, EECS Building
TEL : 734-546-8048
Email: tianpei@umich.edu
• LinkedIn: personal webpage
https://www.linkedin.com/in/tianpeiluke
• Research: details for my research activities
http://tbayes.eecs.umich.edu/tianpei/research_main
• Github: my codes available
https://github.com/TianpeiLuke
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11. Outline Introductions My research and contributions Additional information References
Peter L Bartlett and Shahar Mendelson. Rademacher and Gaussian
complexities: Risk bounds and structural results. The Journal of Machine
Learning Research, 3:463–482, 2003.
Olivier Bousquet and Andr´e Elisseeff. Stability and generalization. The
Journal of Machine Learning Research, 2:499–526, 2002.
Jason Farquhar, David Hardoon, Hongying Meng, John S Shawe-taylor, and
Sandor Szedmak. Two view learning: SVM-2K, theory and practice.
Advances in neural information processing systems, pages 355–362, 2005.
David Hardoon, Sandor Szedmak, and John Shawe-Taylor. Canonical
correlation analysis: An overview with application to learning methods.
Neural computation, 16(12):2639–2664, 2004.
Alfred O Hero. Geometric entropy minimization (GEM) for anomaly detection
and localization. Advances in Neural Information Processing Systems,
pages 585–592, 2006.
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and
Andrew Y Ng. Multimodal deep learning. Proceedings of the 28th
International Conference on Machine Learning (ICML-11), pages 689–696,
2011.
Bernhard Sch¨olkopf, Robert C Williamson, Alex J Smola, John Shawe-Taylor,
and John C Platt. Support vector method for novelty detection. Advances
In Neural Information Processing Systems, 12:582–588, 1999.
David E Tyler. Robust statistics: Theory and methods. Journal of the
American Statistical Association, 103(482):888–889, 2008. 11 / 12

12. Outline Introductions My research and contributions Additional information References
Thank you!
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