Dimensionality Reduction for Fuzzy-Interval Based Explicit Graph Embedding
Résumé
The motivation of this work is to address the problem of lack of efficient computational tools for graph based structural representations. In this paper we take-forward our work on graph embedding to answer two important issues of high dimensionality and sparsity, of the feature vector from our previously proposed fuzzy-interval based explicit graph embedding approach. The latter is a method to embed an attributed graph, with numeric as well as symbolic attributes on both nodes and edges, into a feature vector. We propose to reduced the feature dimensionality through principal component analysis (PCA). The resulting feature vector has interesting properties and enables graph based structural representations to employ the range of efficient state -of-the-art computational models of statistical machine learning. A set of initial graphics recognition experimentation on IAM letter, GREC and fingerprint graph datasets, shows that PCA successfully reduces the feature dimensionality without degrading performance of the original graph embedding technique.