There isn't a MITACS seminar this week, however the following talks might be of interest to you.
Here is the first one at UdeM (see below for the second one at McGill):
BoostCluster: Boosting Clustering by Pairwise Constraints
by Rong Jin, Michigan State University
Location: Pavillon Andre-Aisenstadt (UdeM), room 1177 Time: November 23rd 2007, 14h00
Data clustering is an important task in many disciplines. A large number of studies have attempted to improve clustering by using the side information that is often encoded as pairwise constraints. However, these studies focus on designing special clustering algorithms that can effectively exploit the pairwise constraints. We present a boosting framework for data clustering, termed as BoostCluster, that is able to iteratively improve the accuracy of any given clustering algorithm by exploiting the pairwise constraints. The key challenge in designing a boosting framework for data clustering is how to influence an arbitrary clustering algorithm with the side information since clustering algorithms by definition are unsupervised. The proposed framework addresses this problem by dynamically generating new data representations at each iteration that are, on the one hand, adapted to the clustering results at previous iterations by the given algorithm, and on the other hand consistent with the given side information. Our empirical study shows that the proposed boosting framework is effective in improving the performance of a number of popular clustering algorithms (K-means, partitional SingleLink, spectral clustering), and its performance is comparable to the state-of-the-art algorithms for data clustering with side information.
Also, Rong Jin is giving another talk at McGill on Wednesday 21st, 11h30, McConnell Building (McGill), room 103:
Title: Batch Mode Active Learning
Abstract: The goal of active learning is to select the most informative examples for manual labeling. Most of the previous studies in active learning have focused on selecting a {\em single} unlabeled example in each iteration. This could be inefficient since the classification model has to be retrained for every labeled example. In this paper, we present a framework for ``{\bf batch mode active learning}'' that applies the Fisher information matrix to select a number of informative examples simultaneously. The key computational challenge is how to efficiently identify the subset of unlabeled examples that can result in the largest reduction in the Fisher information. To resolve this challenge, we propose efficient algorithms that are based on bound optimization and the property of submodular functions. Our empirical studies with text categorization and medical image classification show that the proposed batch mode active learning algorithm is more effective than the state-of-the-art algorithms for active learning.