Fundamentals of speech recognition
Fundamentals of speech recognition
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Investigation of Graph Kernels on a Collaborative Recommendation Task
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
Semi-supervised Classification from Discriminative Random Walks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Graph nodes clustering with the sigmoid commute-time kernel: A comparative study
Data & Knowledge Engineering
Network Science: Theory and Applications
Network Science: Theory and Applications
Semisupervised Learning for Computational Linguistics
Semisupervised Learning for Computational Linguistics
A Matrix Handbook for Statisticians
A Matrix Handbook for Statisticians
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
The Sum-over-Paths Covariance Kernel: A Novel Covariance Measure between Nodes of a Directed Graph
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Link Analysis Extension of Correspondence Analysis for Mining Relational Databases
IEEE Transactions on Knowledge and Data Engineering
The Structure of Complex Networks: Theory and Applications
The Structure of Complex Networks: Theory and Applications
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This letter investigates a link-analysis variant of discriminant analysis for projecting nodes of a (partially) labeled graph in a low-dimensional subspace and extracting discriminant node features. Basically, it corresponds to a kernel discriminant analysis computed from a kernel on a graph together with a class betweenness measure. As for standard discriminant analysis, the projected nodes are maximally separated with respect to the ratio of between-class inertia on total inertia - the distances being computed according to the kernel. The visualization of various graphs shows that the resulting display conveys useful information. Moreover, semi-supervised classification experiments indicate that the discriminant analysis indeed extracts relevant node features that are able to classify unlabeled nodes with competing performance.