We present the basic idea of abstract principal component analysis(APCA)as a general approach that extends various popular data analysis techniques such as PCA and GPCA.We describe the mathematical theory behind APCA ...We present the basic idea of abstract principal component analysis(APCA)as a general approach that extends various popular data analysis techniques such as PCA and GPCA.We describe the mathematical theory behind APCA and focus on a particular application to mode extractions from a data set of mixed temporal and spatial signals.For illustration,algorithmic implementation details and numerical examples are presented for the extraction of a number of basic types of wave modes including,in particular,dynamic modes involving spatial shifts.展开更多
This research examines industry-based dissertation research in a doctoralcomputing program through the lens of machine learning algorithms todetermine if natural language processing-based categorization on abstractsal...This research examines industry-based dissertation research in a doctoralcomputing program through the lens of machine learning algorithms todetermine if natural language processing-based categorization on abstractsalone is adequate for classification. This research categorizes dissertationby both their abstracts and by their full-text using the GraphLabCreate library from Apple’s Turi to identify if abstract analysis is anadequate measure of content categorization, which we found was not. Wealso compare the dissertation categorizations using IBM’s Watson Discoverydeep machine learning tool. Our research provides perspectiveson the practicality of the manual classification of technical documents;and, it provides insights into the: (1) categories of academic work createdby experienced fulltime working professionals in a Computing doctoralprogram, (2) viability and performance of automated categorization of theabstract analysis against the fulltext dissertation analysis, and (3) natuallanguage processing versus human manual text classification abstraction.展开更多
基金supported by National Science Foundation of USA(Grant No.DMS101607)
文摘We present the basic idea of abstract principal component analysis(APCA)as a general approach that extends various popular data analysis techniques such as PCA and GPCA.We describe the mathematical theory behind APCA and focus on a particular application to mode extractions from a data set of mixed temporal and spatial signals.For illustration,algorithmic implementation details and numerical examples are presented for the extraction of a number of basic types of wave modes including,in particular,dynamic modes involving spatial shifts.
文摘This research examines industry-based dissertation research in a doctoralcomputing program through the lens of machine learning algorithms todetermine if natural language processing-based categorization on abstractsalone is adequate for classification. This research categorizes dissertationby both their abstracts and by their full-text using the GraphLabCreate library from Apple’s Turi to identify if abstract analysis is anadequate measure of content categorization, which we found was not. Wealso compare the dissertation categorizations using IBM’s Watson Discoverydeep machine learning tool. Our research provides perspectiveson the practicality of the manual classification of technical documents;and, it provides insights into the: (1) categories of academic work createdby experienced fulltime working professionals in a Computing doctoralprogram, (2) viability and performance of automated categorization of theabstract analysis against the fulltext dissertation analysis, and (3) natuallanguage processing versus human manual text classification abstraction.
