Elements of information theory
Elements of information theory
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topology matching for fully automatic similarity estimation of 3D shapes
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Estimation of entropy and mutual information
Neural Computation
Skeleton Based Shape Matching and Retrieval
SMI '03 Proceedings of the Shape Modeling International 2003
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Rotation invariant spherical harmonic representation of 3D shape descriptors
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
An introduction to variable and feature selection
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Object Retrieval using Many-to-many Matching of Curve Skeletons
SMI '05 Proceedings of the International Conference on Shape Modeling and Applications 2005
Curve-Skeleton Properties, Applications, and Algorithms
IEEE Transactions on Visualization and Computer Graphics
Clustering and Embedding Using Commute Times
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhancing 3D mesh topological skeletons with discrete contour constrictions
The Visual Computer: International Journal of Computer Graphics
Reeb graphs for shape analysis and applications
Theoretical Computer Science
Isotree: Tree clustering via metric embedding
Neurocomputing
Describing shapes by geometrical-topological properties of real functions
ACM Computing Surveys (CSUR)
A survey of content based 3D shape retrieval methods
Multimedia Tools and Applications
Pattern Analysis & Applications - Special Issue: Non-parametric distance-based classification techniques and their applications
Feature selection with dynamic mutual information
Pattern Recognition
Graph characteristics from the heat kernel trace
Pattern Recognition
Flow Complexity: Fast Polytopal Graph Complexity and 3D Object Clustering
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Information Theory in Computer Vision and Pattern Recognition
Information Theory in Computer Vision and Pattern Recognition
Gait feature subset selection by mutual information
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Learning Gaussian mixture models with entropy-based criteria
IEEE Transactions on Neural Networks
Scalable discriminant feature selection for image retrieval and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Shape google: Geometric words and expressions for invariant shape retrieval
ACM Transactions on Graphics (TOG)
Estimating redundancy information of selected features in multi-dimensional pattern classification
Pattern Recognition Letters
Editorial: Special issue on Graph-Based Representations in Computer Vision
Computer Vision and Image Understanding
International Journal of Computer Vision
The Journal of Machine Learning Research
Graph matching through entropic manifold alignment
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Probability of error, equivocation, and the Chernoff bound
IEEE Transactions on Information Theory
Information-Theoretic dissimilarities for graphs
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
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Pattern recognition methods often deal with samples consisting of thousands of features. Therefore, the reduction of their dimensionality becomes crucial to make the data sets tractable. Feature selection techniques remove the irrelevant and noisy features and select a subset of features which describe better the samples and produce a better classification performance. In this paper, we propose a novel feature selection method for supervised classification within an information-theoretic framework. Mutual information is exploited for measuring the statistical relation between a subset of features and the class labels of the samples. Traditionally it has been measured for ranking single features; however, in most data sets the features are not independent and their combination provides much more information about the class than the sum of their individual prediction power. We analyze the use of different estimation methods which bypass the density estimation and estimate entropy and mutual information directly from the set of samples. These methods allow us to efficiently evaluate multivariate sets of thousands of features. Within this framework we experiment with spectral graph features extracted from 3D shapes. Most of the existing graph classification techniques rely on the graph attributes. We use unattributed graphs to show what is the contribution of each spectral feature to graph classification. Apart from succeeding to classify graphs from shapes relying only on their structure, we test to what extent the set of selected spectral features are robust to perturbations of the dataset.