Abstract book (pdf) - ICPR 2010

We report performance evaluation of our automatic feature discovery method on the publicly available Gisette dataset: a
set of 29 features discovered by our method ranks 129 among all 411 current entries on the validation set. Our approach
is a greedy forward selection algorithm guided by error clusters. The algorithm finds error clusters in the current feature
space, then projects one tight cluster into the null space of the feature mapping, where a new feature that helps to classify
these errors can be discovered. This method assumes a ``data-rich’’ problem domain and works well when large amount
of labeled data is available. The result on the Gisette dataset shows that our method is competitive to many of the current
feature selection algorithms. We also provide analytical results showing that our method is guaranteed to lower the error
rate on Gaussian distributions and that our approach may outperform the standard Linear Discriminant Analysis (LDA)
method in some cases.
15:00-17:10, Paper MoBT9.56
Optimized Entropy-Constrained Vector Quantization of Lossy Vector Map Compression
Chen, Minjie, Univ. of Eastern Finland
Xu, Mantao, Carestream Health Corp. Shanghai, China
Fränti, Pasi, Univ. of Eastern Finland
Quantization plays an important part in lossy vector map compression, for which the existing solutions are based on either
a fixed size open-loop codebook, or a simple uniform quantization. In this paper, we proposed an entropy-constrained
vector quantization to optimize both the structure and size of the codebook at the same time using a closed-loop approach.
In order to lower the distortion to a desirable level, we exploit two-level design strategy, where the vector quantization
codebook is designed only for most common vectors and the remaining (outlier) vectors are coded by uniform quantization.
15:00-17:10, Paper MoBT9.57
Nonnegative Embeddings and Projections for Dimensionality Reduction and Information Visualization
Zafeiriou, Stefanos, Imperial Coll. of London
Laskaris, Nikolaos, AiiA-Lab. AUTH,
In this paper, we propose novel algorithms for low dimensionality nonnegative embedding of vectorial and/or relational
data, as well as nonnegative projections for dimensionality reduction. We start by introducing a novel algorithm for Metric
Multidimensional Scaling (MMS). We propose algorithms for Nonnegative Locally Linear Embedding (NLLE) and Nonnegative
Laplacian Eigenmaps (NLE). By reformulating the problem of MMS, NLLE and NLE for finding projections
we propose algorithms for Nonnegative Principal Component Analysis (NPCA), for Nonnegative Orthogonal Neighbourhood
Preserving Projections (NONPP) and Nonnegative Orthogonal Locality Preserving Projections (NOLPP). We demonstrate
some first preliminary results of the proposed methods in data visualization.
15:00-17:10, Paper MoBT9.58
Unsupervised Learning from Linked Documents
Guo, Zhen, SUNY at Binghamton
Zhu, Shenghuo, NEC Lab.
Chi, Yun, NEC Lab.
Zhang, Zhongfei, State Univ. of New York, Binghamton
Gong, Yihong, NEC Lab. America, Inc.
Documents in many corpora, such as digital libraries and webpages, contain both content and link information. In a traditional
topic model which plays an important role in the unsupervised learning, the link information is either totally ignored
or treated as a feature similar to content. We believe that neither approach is capable of accurately capturing the relations
represented by links. To address the limitation of traditional topic models, in this paper we propose a citation-topic (CT)
model that explicitly considers the document relations represented by links. In the CT model, instead of being treated as
yet another feature, links are used to form the structure of the generative model. As a result, in the CT model a given document
is modeled as a mixture of a set of topic distributions, each of which is borrowed (cited) from a document that is
related to the given document. We apply the CT model to several document collections and the experimental comparisons
against state-of-the-art approaches demonstrate very promising performances.
- 66 -