Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Engineering
New Covariance-Based Feature Extraction Methods For Classification And Prediction Of High-Dimensional Data, Mopelola Adediwura Sofolahan
New Covariance-Based Feature Extraction Methods For Classification And Prediction Of High-Dimensional Data, Mopelola Adediwura Sofolahan
Open Access Dissertations
When analyzing high dimensional data sets, it is often necessary to implement feature extraction methods in order to capture relevant discriminating information useful for the purposes of classification and prediction. The relevant information can typically be represented in lower-dimensional feature spaces, and a widely used approach for this is the principal component analysis (PCA) method. PCA efficiently compresses information into lower dimensions; however, studies indicate that it is not optimal for feature extraction especially when dealing with classification problems. Furthermore, for high-dimensional data having limited observations, as is typically the case with remote sensing data and nonstationary data such as …
Information Measures For Statistical Orbit Determination, Alinda Kenyana Mashiku
Information Measures For Statistical Orbit Determination, Alinda Kenyana Mashiku
Open Access Dissertations
The current Situational Space Awareness (SSA) is faced with a huge task of tracking the increasing number of space objects. The tracking of space objects requires frequent and accurate monitoring for orbit maintenance and collision avoidance using methods for statistical orbit determination. Statistical orbit determination enables us to obtain estimates of the state and the statistical information of its region of uncertainty given by the probability density function (PDF). As even collision events with very low probability are important, accurate prediction of collisions require the representation of the full PDF of the random orbit state. Through representing the full PDF …