Mississippi State University

About: Mississippi State University is a education organization based out in Starkville, Mississippi, United States. It is known for research contribution in the topics: Population & Catfish. The organization has 14115 authors who have published 28594 publications receiving 700030 citations. The organization is also known as: The Mississippi State University of Agriculture and Applied Science & Mississippi State University of Agriculture and Applied Science.

Why principal component analysis is not an appropriate feature extraction method for hyperspectral data

Abstract: It is a popular practice in the remote sensing community to apply principal component analysis (PCA) on a high dimensional feature space to achieve dimensionality reduction. Typically, there are two primary goals for dimensionality reduction: (i) data compression and (ii) feature extraction for classification purposes. While PCA has been proven to be an optimal method for data compression, it is not necessarily an optimal method for feature extraction, particularly when the features are used in a supervised classifier. This paper addresses the issue of using PCA on hyperspectral data, specifically why PCA is not optimal for dimensionality reduction in target detection and classification applications. The authors provide theoretical and experimental analysis of PCA to demonstrate why and when PCA is not appropriate. There are variations of the Karhunen-Loeve transform that outperform PCA in a supervised classification scheme, and some of these alternative approaches are discussed in this paper.

The role of students' motivation in peer-moderated asynchronous online discussions

Abstract: This study conducted a content analysis of online discussions to understand the nature of computer-supported collaborative learning and discover how students' motivation, which is a crucial factor to the success of collaborative learning, relates with their interaction and knowledge construction in peer-moderated online discussions. Discussion contents from 23 students in an online class were analysed. The results indicated that perceived value, competence and autonomy were critical factors that influenced lower level interactions; intrinsic motivation was the critical factor that influenced the individualistic elaboration interactions, whereas relatedness was the critical factor that influenced the collaborative elaboration interactions. The results also indicated that autonomy and relatedness were the critical factors that influenced the moderation behaviours. The findings suggest that teachers in online classes should promote students' motivation, and more importantly, scaffold student moderators in meaningful learning during peer-moderated online discussions. [ABSTRACT FROM AUTHOR]