Discourse type clustering using POS n-gram profiles and high-dimensional embeddings
EACL '12 Proceedings of the Student Research Workshop at the 13th Conference of the European Chapter of the Association for Computational Linguistics
Graph drawing by classical multidimensional scaling: new perspectives
GD'12 Proceedings of the 20th international conference on Graph Drawing
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The class of Schoenberg transformations, embedding Euclidean distances into higher dimensional Euclidean spaces, is presented, and derived from theorems on positive definite and conditionally negative definite matrices. Original results on the arc lengths, angles and curvature of the transformations are proposed, and visualized on artificial data sets by classical multidimensional scaling. A distance-based discriminant algorithm and a robust multidimensional centroid estimate illustrate the theory, closely connected to the Gaussian kernels of Machine Learning.