1. A Monte Carlo-Based Search Strategy for Dimensionality Reduction in Performance Tuning Parameters

A. Omondi; I.A. Lukandu; G.W. Wanyembi

Articles in Press, Accepted Manuscript, Available Online from 05 July 2020

Abstract
  Redundant and irrelevant features in high dimensional data increase the complexity in underlying mathematical models. It is necessary to conduct pre-processing steps that search for the most relevant features in order to reduce the dimensionality of the data. This study made use of a meta-heuristic search ...  Read More

2. Impact of linear dimensionality reduction methods on the performance of anomaly detection algorithms in hyperspectral images

Mohsen Zare-Baghbidi; Saeid Homayouni; Kamal Jamshidi; A. R. Naghsh-Nilchi

Volume 3, Issue 1 , Winter 2015, , Pages 11-20

Abstract
  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 ...  Read More