Abstract book (pdf) - ICPR 2010

15:00-17:10, Paper MoBT9.25
Gaussian Process Learning from Order Relationships using Expectation Propagation
Wang, Ruixuan, Univ. of Dundee
Mckenna, Stephen James, Univ. of Dundee
A method for Gaussian process learning of a scalar function from a set of pair-wise order relationships is presented. Expectation
propagation is used to obtain an approximation to the log marginal likelihood which is optimised using an analytical
expression for its gradient. Experimental results show that the proposed method performs well compared with a
previous method for Gaussian process preference learning.
15:00-17:10, Paper MoBT9.26
Feature Ranking based on Decision Border
Diamantini, Claudia, Univ. Pol. Delle Marche
Gemelli, Alberto, Univ. Pol. Delle Marche
Potena, Domenico, Univ. Pol. Delle Marche
In this paper a Feature Ranking algorithm for classification is proposed, which is based on the notion of Bayes decision
border. The method elaborates upon the results of the Decision Border Feature Extraction approach, exploiting properties
of eigenvalues and eigenvectors of the orthogonal transformation to calculate the discriminative importance weights of
the original features. Non parametric classification is also considered by resorting to Labeled Vector Quantizers neural
networks trained by the BVQ algorithm. The choice of this architecture leads to a cheap implementation of the ranking algorithm
we call BVQ-FR. The effectiveness of BVQ-FR is tested on real datasets. The novelty of the method is to use a
feature extraction technique to assess the weight of the original features, as opposed to heuristics methods commonly used.
15:00-17:10, Paper MoBT9.27
Three-Layer Spatial Sparse Coding for Image Classification
Dai, Dengxin, Wuhan Univ.
Yang, Wen, Wuhan Univ.
Wu, Tianfu, Lotus Hill Res. Inst.
In this paper, we propose a three-layer spatial sparse coding (TSSC) for image classification, aiming at three objectives:
naturally recognizing image categories without learning phase, naturally involving spatial configurations of images, and
naturally counteracting the intra-class variances. The method begins by representing the test images in a spatial pyramid
as the to-be-recovered signals, and taking all sampled image patches at multiple scales from the labeled images as the
bases. Then, three sets of coefficients are involved into the cardinal sparse coding to get the TSSC, one to penalize spatial
inconsistencies of the pyramid cells and the corresponding selected bases, one to guarantee the sparsity of selected images,
and the other to guarantee the sparsity of selected categories. Finally, the test images are classified according to a simple
image-to-category similarity defined on the coding coefficients. In experiments, we test our method on two publicly available
datasets and achieve significantly more accurate results than the conventional sparse coding with only a modest increase
in computational complexity.
15:00-17:10, Paper MoBT9.28
Theoretical Analysis of a Performance Measure for Imbalanced Data
Garcia, Vicente, Univ. Jaume I
Mollineda, Ramón A., Univ. Jaume I
Sanchez, J. Salvador, Univ. Jaume I
This paper analyzes a generalization of a new metric to evaluate the classification performance in imbalanced domains,
combining some estimate of the overall accuracy with a plain index about how dominant the class with the highest individual
accuracy is. A theoretical analysis shows the merits of this metric when compared to other well-known measures.
15:00-17:10, Paper MoBT9.29
Cluster Preserving Embedding
Zhan, Yubin, National Univ. of Defense Tech.
Yin, Jianping, National Univ. of Defense Tech.
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