Authors:
Markus Heinonen
;
Niko Välimäki
;
Veli Mäkinen
and
Juho Rousu
Affiliation:
University of Helsinki, Finland
Keyword(s):
Graph kernels, Compressed data structures, XBW transform, Reaction graph, Hierarchical classification.
Related
Ontology
Subjects/Areas/Topics:
Algorithms and Software Tools
;
Bioinformatics
;
Biomedical Engineering
;
Data Mining and Machine Learning
;
Pattern Recognition, Clustering and Classification
;
Systems Biology
Abstract:
Kernels for structured data are rapidly becoming an essential part of the machine learning toolbox. Graph kernels provide similarity measures for complex relational objects, such as molecules and enzymes. Graph kernels based on walks are popular due their fast computation but their predictive performance is often not satisfactory, while kernels based on subgraphs suffer from high computational cost and are limited to small substructures. Kernels based on paths offer a promising middle ground between these two extremes. However, the computation of path kernels has so far been assumed computationally too challenging. In this paper we introduce an effective method for computing path based kernels; we employ a Burrows-Wheeler transform based compressed path index for fast and space-efficient enumeration of paths. Unlike many kernel algorithms the index representation retains fast access to individual features. In our experiments with chemical reaction graphs, path based kernels surpass s
tate-of-the-art graph kernels in prediction accuracy.
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