Fractional Component Analysis (FCA) for Mixed Signals
This paper proposes the fractional component analysis (FCA), whose
goal is to decompose the observed signal into component signals and
recover their fractions. The uniqueness of our idea in comparison with
other similar methods is the concept of the virtual PDF (probability
distribution function) that models signal mixing on the sensor. In
this paper, we derive the virtual PDF based on positivity constraint,
unity constraint, and randomness assumption, and we then build it into
the mixture density model. In order to learn parameters of this model
from data using EM (Expectation-Maximization) algorithm, the key point
is to derive the approximation of the virtual PDF using its cumulants.
Finally we illustrate experimental results on synthetic data to show
the unique decision boundary obtained from our method.
文献情報
Asanobu KITAMOTO,
"Fractional Component Analysis (FCA) for Mixed Signals",
Proceedings of the 16th International Conference on Pattern Recognition (ICPR'02),
Vol. 3, pp. 383-386, IEEE, doi:10.1109/ICPR.2002.1047925,
2002年8月
(in English)
BibTeX フォーマット
@InProceedings{ k:icpr02,
author = {Asanobu KITAMOTO},
title = {Fractional Component Analysis (FCA) for Mixed Signals},
booktitle = {Proceedings of the 16th International Conference on Pattern Recognition (ICPR'02)},
volume = {3},
pages = {383-386},
publisher = {IEEE},
year = 2002,
month = 8,
doi = {10.1109/ICPR.2002.1047925},
note = { (in English)},
}
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