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AI COMPUTATION OF L1-NORM-ERROR PRINCIPAL COMPONENTS WITH APPLICATIONS TO TRAINING DATASET CURATION AND DETECTION OF CHANGE
| Title: | AI COMPUTATION OF L1-NORM-ERROR PRINCIPAL COMPONENTS WITH APPLICATIONS TO TRAINING DATASET CURATION AND DETECTION OF CHANGE. |
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| Name(s): |
Varma, Kavita , author Pados, Dimitris, Thesis advisor Florida Atlantic University, Degree grantor Department of Computer and Electrical Engineering and Computer Science College of Engineering and Computer Science |
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| Type of Resource: | text | |
| Genre: | Electronic Thesis Or Dissertation | |
| Date Created: | 2024 | |
| Date Issued: | 2024 | |
| Publisher: | Florida Atlantic University | |
| Place of Publication: | Boca Raton, Fla. | |
| Physical Form: | application/pdf | |
| Extent: | 107 p. | |
| Language(s): | English | |
| Abstract/Description: | The aim of this dissertation is to achieve a thorough understanding and develop an algorithmic framework for a crucial aspect of autonomous and artificial intelligence (AI) systems: Data Analysis. In the current era of AI and machine learning (ML), ”data” holds paramount importance. For effective learning tasks, it is essential to ensure that the training dataset is accurate and comprehensive. Additionally, during system operation, it is vital to identify and address faulty data to prevent potentially catastrophic system failures. Our research in data analysis focuses on creating new mathematical theories and algorithms for outlier-resistant matrix decomposition using L1-norm principal component analysis (PCA). L1-norm PCA has demonstrated robustness against irregular data points and will be pivotal for future AI learning and autonomous system operations. This dissertation presents a comprehensive exploration of L1-norm techniques and their diverse applications. A summary of our contributions in this manuscript follows: Chapter 1 establishes the foundational mathematical notation and linear algebra concepts critical for the subsequent discussions, along with a review of the complexities of the current state-of-the-art in L1-norm matrix decomposition algorithms. In Chapter 2, we address the L1-norm error decomposition problem by introducing a novel method called ”Individual L1-norm-error Principal Component Computation by 3-layer Perceptron” (Perceptron L1 error). Extensive studies demonstrate the efficiency of this greedy L1-norm PC calculator. | |
| Identifier: | FA00014460 (IID) | |
| Degree granted: | Dissertation (PhD)--Florida Atlantic University, 2024. | |
| Collection: | FAU Electronic Theses and Dissertations Collection | |
| Note(s): | Includes bibliography. | |
| Subject(s): |
Artificial intelligence Machine learning Neural networks (Computer science) Data Analysis |
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| Persistent Link to This Record: | http://purl.flvc.org/fau/fd/FA00014460 | |
| Use and Reproduction: | Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. | |
| Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
| Host Institution: | FAU |
