| タイトル | Real-Time Principal-Component Analysis |
| 本文（外部サイト） | http://hdl.handle.net/2060/20110014920 |
| 著者（英） | Duong, Vu; Duong, Tuan |
| 著者所属（英） | California Inst. of Tech. |
| 発行日 | 2005-06-01 |
| 言語 | eng |
| 内容記述 | A recently written computer program implements dominant-element-based gradient descent and dynamic initial learning rate (DOGEDYN), which was described in Method of Real-Time Principal-Component Analysis (NPO-40034) NASA Tech Briefs, Vol. 29, No. 1 (January 2005), page 59. To recapitulate: DOGEDYN is a method of sequential principal-component analysis (PCA) suitable for such applications as data compression and extraction of features from sets of data. In DOGEDYN, input data are represented as a sequence of vectors acquired at sampling times. The learning algorithm in DOGEDYN involves sequential extraction of principal vectors by means of a gradient descent in which only the dominant element is used at each iteration. Each iteration includes updating of elements of a weight matrix by amounts proportional to a dynamic initial learning rate chosen to increase the rate of convergence by compensating for the energy lost through the previous extraction of principal components. In comparison with a prior method of gradient-descent-based sequential PCA, DOGEDYN involves less computation and offers a greater rate of learning convergence. The sequential DOGEDYN computations require less memory than would parallel computations for the same purpose. The DOGEDYN software can be executed on a personal computer. |
| NASA分類 | Man/System Technology and Life Support |
| レポートNO | NPO-40056 |
| 権利 | Copyright, Distribution as joint owner in the copyright |
| URI | https://repository.exst.jaxa.jp/dspace/handle/a-is/270471 |
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