|
Le Song PhD
Student, The Statistical
Machine Learning Program, National
ICT Thesis Advisor:
Alex Smola E-Mail: Phone: +61 401 946 960 |
CV Publications Software Datasets Links
Research Interests
Machine
learning, kernel methods and information visualization. Applications to biological
and social data analysis.
Learning via Hilbert Space Embedding of Distributions
This
is a framework of methods which allow us to compute distances between
distributions without the need for intermediate density estimation. Moreover,
these methods allow algorithm designers to specify which properties of a
distribution are most relevant to their problems. Basically, distributions are
embedded into Hilbert spaces via mean maps, and then all subsequent operations on
distributions are carried out in the Hilbert space. This often leads to algorithms
which are simpler and more effective than information theoretic methods in a
broad range of applications.
Learning via Dependence
Estimation
This research aims to
develop a learning framework based on statistical dependence estimation. Many
learning tasks can be cast into this framework: for instance, classification can
be treated as learning a function such that the dependence between the
predicted labels and the given labels are maximized; clustering can be viewed
as generating the labels such that their dependence on the data is maximized.
Besides classification and clustering, the dependence estimation view of
learning can be applied to a variety of other learning tasks, such as feature
selection, data point selection and dimensionality reduction. When expressing
the dependence as the square of the Hilbert-Schmidt norm of the
cross-covariance operator, this framework recovers many existing algorithms as
special cases¡ªthey are different only in their choice of kernels. By choosing
an appropriate kernel, this framework also leads to many new and interesting
algorithms.
Biomedical
Signal Processing and Brain-Computer Interface
Brain-computer
interface (BCI) is a communication system that relies on the brain rather than
the body for control and feedback. My research employs a novel type of features
based explicitly on the neurophysiology of EEG signals for classification.
Basically, EEG signals are considered as the outputs of a networked dynamical
system. The nodes of this system consist of cortical patches, while the links
correspond to neural fibers. A large and complex system like this often
generates interesting collective dynamics, such as synchronization in the
activities of the nodes, and they result in the change of EEG patterns measured
on the scalp. These features from the collective dynamics of the system are employed
for classification.
Visualizing
Biological and Social Networks
Much of the world¡¯s
information has a relational structure and can be modelled mathematically as
graphs. Examples include webgraphs, social networks, and biological
networks. Recent discoveries show that many of these large and complex
networks exhibit the small world phenomenon and follow a power-law degree
distribution. Traditional graph drawing algorithms based on random graph models
thus fail to produce an effective visualisation for these networks. We designed
new graph drawing algorithms which take advantage of the above mentioned two
emergent properties.
1.
L. Song, X. Zhang, A. Smola, A. Gretton and B.
Schoelkopf, ¡°Tailoring density estimation via reproducing kernel moment
matching,¡± 25th International Conference on Machine Learning (ICML 2008).
2.
S. Kuan, J. Gatt, C. Dobson-Stone, D. Palmer, R.
Paul, L. Song, E. Gordon, P. Schofield and L. Williams, ¡°A polymorphism of the
MAOA gene is associated with emotional brain and behaviour makers of antisocial
and psychopathic personality traits,¡± (submitted to the Journal of
Neuroscience).
3.
L. Song, A. Smola, K. Borgwardt and A. Getton,
¡°Colored maximum variance unfolding,¡± Neural Information Processing Systems 2007 (NIPS 07). (Full Oral
Presentation). [pdf][appendix]
4.
Gretton, K. Fukumizu, C.H. Teo, L. Song, B.
Schoelkopf and A. Smola, ¡°A kernel statistical test of independence,¡± Neural Information Processing Systems 2007 (NIPS 07). (Poster Spotlight).
5.
L. Song, A. Smola, A. Gretton, J. Bedo and K.
Borgwardt, ¡°Feature selection via dependence maximization,¡± Journal of Machine Learning Researches. [submitted][preprint]
6.
Smola, A. Gretton, K. Borgwardt, L. Song and B.
Scheolkopf, ¡°A Hilbert space embedding for distributions,¡± 18th
International Conference on Algorithmic Learning Theory (ALT 2007). [pdf]
7.
L. Song, J. Bedo, K. Borgwardt, A. Getton and A.
Smola, ¡°Gene selection via the BAHSIC family of algorithms,¡± 15th Intl.
Conference on Intelligent Systems for Molecular Biology (ISMB 2007). [preprint][supplementary]
8.
L. Song, A. Smola, Arthur Gretton, K. Borgwardt and
J. Bedo, ¡°Supervised feature selection via dependence estimation,¡± 24th International
Conference on Machine Learning (ICML 2007). (long version or technical report [pdf])
9.
L. Song, A. Smola, Arthur Gretton and K. Borgwardt,
¡°A dependence maximization view of clustering,¡± 24th International Conference on Machine Learning (ICML 2007). (long version or technical report [pdf])
10. L. Williams, D. Palmer, B. Liddell, L. Song and E. Gordon, ¡°The ¡®when¡¯
and ¡®where¡¯ of perceiving signals of threat versus non-threat,¡± NeuroImage, vol 31, pp. 458¨C467, 2006. [link]
11.
L. Song, and J. Epps,
¡°Classifying EEG for brain-computer interfaces: learning optimal filters for
dynamical system features¡±, 23rd International Conference on Machine Learning
(ICML 2006). [pdf]
12.
L. Song, and J. Epps,
¡°Improving the separability of EEG signals during motor imagery with an
efficient circular Laplacian¡±, 31st IEEE
International Conference on Acoustics, Speech, and Signal Processing (ICASSP
2006). [pdf]
13.
L. Song, E. Gordon, and E.
Gysels, ¡°Phase Synchrony Rate for the Recognition of Motor Imagery in BCIs¡±, Neural Information Processing Systems 2005 (NIPS 05). [pdf]
14.
L. Song,
¡°Desynchronization network analysis for the recognition of imagined movement,¡± 27th IEEE EMBS Annual International Conference, 2005. [pdf]
15.
W. Huang, C. Murray, X.
Shen, L. Song, Y.X. Wu, and L. Zheng, ¡°Visualization and analysis of network
motifs¡±, 9th International Conference
on Information Visualization (IV 2005), 2005.
[pdf]
16.
Ahmed, T. Dywer, S.H.
Hong, C. Murray, L. Song, and Y.X. Wu, ¡°Visualization and analysis of large and
complex scale-free networks,¡± IEEE VGTC Symposium
on Visualization (EUROGRAPHICS), 2005.
[pdf]
17.
L. Song, and M.
Takatsuka, ¡°Real-time 3d finger pointing for an augmented desk,¡± 6th Australasian User Interface Conference, CRPIT 40,
2005. [pdf]
18.
L. Zheng, L. Song and P. Eades, ¡°Crossing minimization problems of drawing
bipartite graphs in two clusters,¡± Asian-Pacific
Symposium on Information Visualization, CRPIT 45, 2005. [pdf]
19.
Ahmed, T. Dywer, S.H.
Hong, C. Murray, L. Song, and Y.X. Wu, ¡°Wilmascope graph visualization,¡± IEEE Information Visualization (InfoVis), 2004. [pdf][link]
Earlier Work
1. S.Q. Liu, and L. Song, ¡°Curvature relation of wave front and wave
changing in external field,¡± Applied Mathematics
and Mechanics, 26(7), 2005. [Chinese draft pdf]
2. S.Q. Liu, and L. Song, ¡°The numerical analysis of
Lobster stomatogastric nervous system,¡± Acta Biophysica Sinica, 20(3), 2004. [Chinese pdf]
3. L. Song, B. Jiang, and Y.L. Zhu,
¡°The waterways¡ªa certain future,¡± The Interdisciplinary
Contest in Modeling (hosted
by the Consortium for Mathematics and its Application, and NSF), 2001. [pdf]
¡¤
BAHSIC: backward elimination for feature selection via
dependence estimation. Support linear, nonlinear, binary, multiclass and
regression feature selection. (A prototype
written in Python)
¡¤
CLUHSIC: clustering via dependence estimation. An additional
metric can be applied on the cluster labels. (Written in C and examples given
in Matlab)
¡¤
MUHSIC: dimensionality reduction via dependence estimation.
Side information can be incorporated into the visualization. (A mix of Matlab
and C)
¡¤
Incomplete Cholesky
Decomposition: linearize the
kernel matrix for a nonlinear kernel. (Written in Python)
¡¤
Other codes in ELEFANT.
¡¤
Clustering
¡¤
Alex Smola
¡¤
Quoc Viet Le
¡¤
Xinhua Zhang
¡¤
Ying Xin Wu