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Proceedings Paper

Adaptive data reduction with improved information association
Author(s): Vahid R. Riasati; Wenhue Gao
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Paper Abstract

A class of adaptive data compression routines is presented based on data dependent transformation. The current class of methods identifies improved information association by utilization of eigen-vectors rather than the eigen-values based prioritization. The Karhounen-Loeve, KL, transform limits the importance of the data by the limitation of the eigen-values associated with covariance of data, this leads to the truncation of the eigenvectors by their energy levels. Therefore, the remaining data in the KL transform method limits the information that can be represented to those data contents that represent the majority of signal energy as identified by the prioritized eigen-values of the data covariance matrix. The method presented in this work retains desired data structure and enables a more exact representation of the information in the data leading to the preservation of data that can contain significantly relevant information even-though its energy contents may be relatively low as compared to other bases. This work presents a description of this idea along with an error analysis relative to the original data. The simulation work applies the technique to data from an image by association of neighboring data samples. A discussion of the simulation results though relevant metrics completes the analysis in this work.

Paper Details

Date Published: 18 May 2012
PDF: 13 pages
Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 839218 (18 May 2012); doi: 10.1117/12.916835
Show Author Affiliations
Vahid R. Riasati, MacAulay-Brown, Inc. (United States)
Wenhue Gao, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 8392:
Signal Processing, Sensor Fusion, and Target Recognition XXI
Ivan Kadar, Editor(s)

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