We propose a novel approach for improving Feature Selection for Word Sense Disambiguation by incorporating a feature relevance prior for each word indicating which features are more likely to be selected. We use transfer of knowledge from similar words to learn this prior over the features, which permits us to learn higher accuracy models, particularly for the rarer word senses. Results on the OntoNotes verb data show significant improvement over the baseline feature selection algorithm and results that are comparable to or better than other state-of-the-art methods.

@InProceedings{dhillon_acl09,
  author    = {Dhillon, Paramveer S.  and  Ungar, Lyle H.},
  title     = {Transfer Learning, Feature Selection and Word Sense Disambiguation},
  booktitle = {Proceedings of the ACL-IJCNLP 2009 Conference Short Papers},
  month     = {August},
  year      = {2009},
  address   = {Suntec, Singapore},
  publisher = {Association for Computational Linguistics},
  pages     = {257--260},
  url       = {http://www.aclweb.org/anthology/P/P09/P09-2065}
}

We address the problem of joint feature selection in multiple related classification or regression tasks. When doing feature selection across multiple tasks, usually one can ``borrow strength" across these tasks to get a more sensitive criterion for deciding which features to select. We propose a novel method, the Multiple Inclusion Criterion (MIC), which can be used in stepwise feature selection to improve feature selection across multiple related tasks. Our approach allows each feature to be added to none, some, or all of the tasks. MIC is most beneficial for selecting a small set of predictive features from a large pool of features, as is common in genomic and biological datasets. Experimental results on such datasets show that MIC usually outperforms other competing multi-task learning methods not only in terms of accuracy but also by building simpler and more interpretable models.

@inproceedings{dhillon_ecml09,
       author = {Paramveer S. Dhillon and Brian Tomasik and Dean Foster and Lyle Ungar},
       title = {Multi-Task Feature Selection Using The Multiple Inclusion Criterion (MIC)},
       booktitle = {European Conference on Machine Learning (ECML)-PKDD},
       publisher = {Springer},
       series    = {Lecture Notes in Computer Science},
       month = {September},
       year = {2009},
       city = {Bled},
       country = {Slovenia}
}

We present an information theoretic approach to feature selection when the data possesses feature classes. Feature classes are pervasive in real data. For example, in gene expression data, the genes which serve as features may be divided into classes based on their membership in gene families or pathways. When doing word sense disambiguation or named entity extraction, features fall into classes including adjacent words, their parts of speech, and the topic and venue of the document the word is in. When predictive features occur predominantly in a small number of feature classes, our information theoretic approach significantly improves feature selection. Experiments on real and synthetic data demonstrate substantial improvement in predictive accuracy over the standard $\ell_0$ penalty-based stepwise and streamwise feature selection methods as well as over Lasso and Elastic Nets, all of which are oblivious to the existence of feature classes.

@inproceedings{dhillonICDM08,
  author    = {Paramveer S. Dhillon and Dean Foster and Lyle H. Ungar},
  title     = {Efficient Feature Selection in the Presence of Multiple Feature Classes},
  booktitle = {ICDM},
  year      = {2008},
  pages     = {779-784},
  ee        = {http://dx.doi.org/10.1109/ICDM.2008.56},
  crossref  = {DBLP:conf/icdm/2008}
}

An important cue to high level scene understanding is to analyze the objects in the scene and their behavior and interactions. In this paper, we study the problem of classification of activities in videos, as this is an integral component of any scene understanding system, and present a novel approach for recognizing human action categories in videos by combining information from appearance and motion of human body parts. Our approach is based on tracking human body parts by using mixture particle filters and then clustering the particles using local non - parametric clustering, hence associating a local set of particles to each cluster mode. The trajectory of these cluster modes provides the ``motion'' information and the ``appearance'' information is provided by the statistical information about the relative motion of these local set of particles over a number of frames. Later we use a ``Bag of Words" model to build one histogram per video sequence from the set of these robust appearance and motion descriptors. These histograms provide us characteristic information which helps us to discriminate among various human actions which ultimately helps us in better understanding of the complete scene. We tested our approach on the standard KTH and Weizmann human action datasets and the results were comparable to the state of the art methods. Additionally our approach is able to distinguish between activities that involve the motion of complete body from those in which only certain body parts move. In other words, our method discriminates well between activities with ``global body motion" like running, jogging etc. and ``local motion" like waving, boxing etc.

@article{ dhillonCVPR-VISU09,
author = {P.S. Dhillon and S. Nowozin and C.H. Lampert},
title = {Combining appearance and motion for human action classification in videos},
journal ={Computer Vision and Pattern Recognition Workshop},
volume = {0},
year = {2009},
isbn = {978-1-4244-3994-2},
pages = {22-29},
doi = {http://doi.ieeecomputersociety.org/10.1109/CVPR.2009.5204237},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}

This paper addresses the problem of facial feature detection and tracking in real-time using a single active camera. The variable parameters of the camera (i.e. pan, tilt and zoom) are changed adaptively to track the face of the agent in successive frames and detect the facial features which may be used for facial expression analysis for surveillance or mesh generation for animation purposes, at a later stage. Our track- ing procedure assumes planar motion of the face. It also detects invalid feature points i.e. those feature points which do not correspond to actual facial features, but are outliers. They are subsequently abandoned by our procedure in order to extract ``high level'' information from the face for facial mesh generation or emotion recognition which might be helpful for Video Surveillance purposes. The only limitation on the performance of the procedure is imposed by the maximum pan/tilt range of the camera.

@inproceedings{dhillon_cisis,
       author = {Paramveer S. Dhillon},
       title = {Robust Real-Time Face Tracking Using an Active Camera},
       booktitle = {Proceedings of 2nd International Workshop on CISIS},
       publisher = {Springer},
       series    = {Advances in Intelligent and Soft Computing , Vol. 63},
       month = {September},
       year = {2009},
       isbn = {978-3-642-04090-0},
       country={Spain}
}