[Lisa_seminaires] 2 interesting talks this week

Hugo Larochelle larocheh at IRO.UMontreal.CA
Mar 20 Nov 09:28:13 EST 2007


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.


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