Recursive principal component analysis of graphs

Authors:
Alessio Micheli;Alessandro Sperduti
Affiliations:
Department of Computer Science, University of Pisa, Italy;Department of Pure and Applied Mathematics, University of Padova, Italy
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

Citing 6
Cited 2

Learning with Recurrent Neural Networks

Learning with Recurrent Neural Networks
The principled design of large-scale recursive neural network architectures--dag-rnns and the protein structure prediction problem

The Journal of Machine Learning Research
A survey of kernels for structured data

ACM SIGKDD Explorations Newsletter
Exact solutions for recursive principal components analysis of sequences and trees

ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Supervised neural networks for the classification of structures

IEEE Transactions on Neural Networks
A general framework for adaptive processing of data structures

IEEE Transactions on Neural Networks

Efficient Computation of Recursive Principal Component Analysis for Structured Input

ECML '07 Proceedings of the 18th European conference on Machine Learning
PCA-Based Representations of Graphs for Prediction in QSAR Studies

ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

Treatment of general structured information by neural networks is an emerging research topic. Here we show how representations for graphs preserving all the information can be devised by Recursive Principal Components Analysis learning. These representations are derived from eigenanalysis of extended vectorial representations of the input graphs. Experimental results performed on a set of chemical compounds represented as undirected graphs show the feasibility and effectiveness of the proposed approach.