Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/MIS.2002.1005631 | IEEE Intelligent Systems |
Keywords | DocType | Volume |
data compression technique,compression technique,multidimensional mean-force,binned distribution,mean-force potential,multidimensional distribution analysis,expansion coefficient,multidimensional distribution,knowledge-based mean-force potential,dct-based image data compression,data compression,spherical harmonics | Journal | 17 |
Issue | ISSN | Citations |
3 | 1094-7167 | 2 |
PageRank | References | Authors |
0.43 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kentaro Onizuka | 1 | 33 | 12.78 |
| Tamotsu Noguchi | 2 | 146 | 26.12 |
| Yutaka Akiyama | 3 | 172 | 37.62 |
| Hideo Matsuda | 4 | 241 | 55.02 |