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http://dx.doi.org/10.5573/ieie.2014.51.2.148

A Study on Classification of Waveforms Using Manifold Embedding Based on Commute Time  

Hahn, Hee-Il (Department of Information and Communications Engineering, Hankuk University of Foreign Studies)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.51, no.2, 2014 , pp. 148-155 More about this Journal
Abstract
In this paper a commute time embedding is implemented by organizing patches according to the graph-based metric, and its properties are investigated via changing the number of nodes on the graph.. It is shown that manifold embedding methods generate the intrinsic geometric structures when waveforms such as speech or music instrumental sound signals are embedded on the low dimensional Euclidean space. Basically manifold embedding algorithms only project the training samples on the graph into an embedding subspace but can not generalize the learning results to test samples. They are very effective for data clustering but are not appropriate for classification or recognition. In this paper a commute time guided transform is adopted to enhance the generalization ability and its performance is analyzed by applying it to the classification of 6 kinds of music instrumental sounds.
Keywords
Manifold learning; Commute time; Manifold embedding; Commute time embedding; Commute time-guided transform;
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