Name
Abstract | ||
|---|---|---|
In statistical methods, such as statistical static timing analysis, Gaussian mixture model (GMM) is a useful tool for representing a non-Gaussian distribution and handling correlation easily. In order to repeat various statistical operations such as summation and maximum for GMMs efficiently, the number of components should be restricted around two. In this paper, we propose a method for reducing the number of components of a given GMM to two (2-GMM). Moreover, since the distribution of each component is represented often by a linear combination of some explanatory variables, we propose a method to compute the covariance between each explanatory variable and the obtained 2-GMM, that is, the sensitivity of 2-GMM to each explanatory variable. In order to evaluate the performance of the proposed methods, we show some experimental results. The proposed methods minimize the normalized integral square error of probability density function of 2-GMM by the sacrifice of the accuracy of sensitivities of 2-GMM. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1587/transfun.E99.A.2425 | IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES |
Keywords | Field | DocType |
Gaussian mixture model, reduction of components, normalized integral square error, sensitivity, statistical method | Search engine,Chemical substance,Algorithm,Mathematics,Mixture model,Imagination | Journal |
Volume | Issue | ISSN |
E99A | 12 | 0916-8508 |
Citations | PageRank | References |
1 | 0.40 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naoya Yokoyama | 1 | 1 | 0.40 |
| Daiki Azuma | 2 | 2 | 0.79 |
| Shuji Tsukiyama | 3 | 85 | 19.66 |
| M. Fukui | 4 | 11 | 3.95 |