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<p>The authors propose a method for multidimensional distribution analysis using a data compression technique. The method avoids the explosion in number of parameters (or coefficients) representing a multidimensional distribution even when the distribution has many dimensions (up to six dimensions or more). In the method, a multidimensional distribution is linearly expanded into a set of expansion coefficients. The expansion procedure neglects high-order cross-terms and reduces the total number of coefficients representing the distribution. This compression technique resemble DCT-based image data compression for computer vision.The authors applied the method to the knowledge-based mean-force potentials between residues for the analysis of protein sequence structure compatibility. They obtain the mean-force potentials by the multidimensional distribution of relative configurations (essentially 6D) between residues. The performance of the multidimensional mean-force potentials measured by native-structure-recognition tests was proved much higher than the performance of conventional 1D distance-based potentials derived from binned distributions.</p>
linear compression, mean-force potentials, multidimensional distribution, spherical Bessel, spherical harmonics
Kentaro Onizuka, Tamotsu Noguchi, Yutaka Akiyama, Hideo Matsuda, "Using Data Compression for Multidimensional Distribution Analysis", IEEE Intelligent Systems, vol. 17, no. , pp. 48-54, May/June 2002, doi:10.1109/MIS.2002.1005631
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