Incremental Dimensionality Reduction in Hyperspectral Data
نویسندگان
چکیده
منابع مشابه
Incremental Dimensionality Reduction in Hyperspectral Data
Conventionally, pattern recognition problems involve both samples and features that get collected over time or that gets generated from distributed sources. The system starts to falter when the number of features reaches a certain threshold and exhibits the curse of dimensionality. Traditionally dimensionality reduction (DR) is performed to prevent the curse of dimensionality when all features ...
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Hyperspectral data contain a large volume of information. This abundance of data is hard to exploit due to high computational cost involved in processing this data. Dimensionality reduction deals with transforming high dimensional data in to lower dimensional space without losing significance of the High dimensional data. In this paper, a new methodology has been proposed that is based on exist...
متن کاملTitle of dissertation : Dimensionality Reduction for Hyperspectral Data
Title of dissertation: Dimensionality Reduction for Hyperspectral Data David P. Widemann, Doctor of Philosophy, 2008 Dissertation directed by: Professor John Benedetto Department of Mathematics Professor Wojciech Czaja Department of Mathematics This thesis is about dimensionality reduction for hyperspectral data. Special emphasis is given to dimensionality reduction techniques known as kernel e...
متن کاملImpact of linear dimensionality reduction methods on the performance of anomaly detection algorithms in hyperspectral images
Anomaly Detection (AD) has recently become an important application of hyperspectral images analysis. The goal of these algorithms is to find the objects in the image scene which are anomalous in comparison to their surrounding background. One way to improve the performance and runtime of these algorithms is to use Dimensionality Reduction (DR) techniques. This paper evaluates the effect of thr...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2017
ISSN: 0975-8887
DOI: 10.5120/ijca2017913575