
Fractional Component Analysis (FCA) 


1. Summary
The problem of reconstructing component signals from the observed
mixed signal is a problem found in many scientific disciplines, and
hence has the wide range of applications. The contribution of this
research, namely Fractional Component Analysis (FCA), is to establish
a stochastic model for the case where component fractions are unity
convex combination and stochastic. We also demonstrate that our
stochastic model leads to an interesting probability distribution
function, and this function is useful for the modelling of, for
example, remote sensing images.
2. Fractional Component Analysis (FCA)
The goal of the "Fractional component analysis (FCA)" is to analyze
the observed signal generated by the linear mixing process of unknown
fractional component signals. Based on simple assumptions on the
probability distributions of component signals and fractions, we
derive a new type of distribution, which we call "blended
distribution," and characterize this model in terms of moments and
cumulants. Its higherorder cumulants indicate that it is in fact
either supergaussian or subgaussian even if the distribution of
component signals are gaussian. Finally we show methods for recovering
fractional components from the observed signal. The background of this
motivation is in the problem of "mixed pixel (mixel) analysis."

Asanobu KITAMOTO,
"Fractional Component Analysis (FCA) for Mixed Signals",
Proceedings of the 16th International Conference on Pattern Recognition (ICPR'02),
Vol. 3,
pp. 383386,
IEEE,
doi:10.1109/ICPR.2002.1047925,
20028
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Abstract
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Asanobu KITAMOTO,
"FCA: The Fractional Component Analysis",
Proceedings of the 4th Workshop on InformationBased Induction Sciences (IBIS 2001),
pp. 297302,
20018
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Abstract
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Paper
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