In these years, we often deal with an enormous amount of data or a data stream in a large variety of pattern recognition tasks. As a promising approach for economising the amount, we have previously defined a volume prototype as a geometric configuration that represents some data points inside and proposed a single-pass algorithm for finding them. In this paper, we analyze the convergence behavior of volume prototypes in high-dimensional cases. In addition, we show the applicability of volume prototypes to high-dimensional classification problems.

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Behavior Analysis of Volume Prototypes in High Dimensionality

The authors have proposed volume prototypes as a compact expression of a huge data or a data stream, along with a one-pass algorithm to find them. A reasonable number of volume prototypes can be used, instead of an enormous number of data, for many applications including classification, clustering and density estimation. In this paper, two algorithms using volume prototypes, called VKM and VEM, are introduced for clustering and density estimation. Compared with the other algorithms for such a huge data, we showed that our algorithms were advantageous in speed of processing, while keeping the same degree of performance, and that both applications were available from the same set of volume prototypes.

Clustering and Density Estimation for Streaming Data using Volume Prototypes

Information Compression by Volume Prototypes

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Information Compression by Volume Prototypes

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Behavior Analysis of Volume Prototypes in High Dimensionality

Clustering and Density Estimation for Streaming Data using Volume Prototypes

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