Supervised Class-Pairwise Nmf for Data Representation and Classification

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Supervised Class-Pairwise Nmf for Data Representation and Classification 7 Pages Posted: 23 May 2022 See all articles by Rachid HedjamRachid HedjamSultan Qaboos UniversityAbdelhamid AbdesselamSultan Qaboos UniversitySaeid NahavandiDeakin UniversityImran KhanSultan Qaboos UniversityMohamed Cherietaffiliation not provided to SSRN Abstract Various Non-negative Matrix factorization (NMF) based methods add new terms to the cost function to adapt the model to specific tasks (e.g., clustering) or to preserve some structural properties in the reduced space (e.g., local invariance). The added term is mainly weighted by a hyper-parameter to control the balance of the overall formula to guide the optimization process towards the objective. The result is a parameterized NMF method. However, NMF method adopts unsupervised approaches to estimate the factorizing matrices. Thus, the ability to perform prediction (e.g. classification) using the new obtained features is not guaranteed. The objective of this work is to design an evolutionary framework to learn the hyper-parameter of the parameterized NMF and estimate the factorizing matrices in a supervised way to be more suitable for classification problems. Moreover, we claim that applying NMF-based algorithms separately to different class-pairs instead of applying it once to the whole dataset improves the effectiveness of the matrix factorization process. This results in training multiple parameterized NMF algorithms with different balancing parameter values. A cross-validation combination learning framework is adopted and a Genetic Algorithm is used to identify the optimal set of hyper-parameter values. The experiments we conducted on both real and synthetic datasets demonstrated the effectiveness of the proposed approach. Keywords: NMF, supervised NMF, parameterized NMF, class-pairwise NMF, Supervised learning, Data representation Suggested Citation: Suggested Citation Hedjam, Rachid and Abdesselam, Abdelhamid and Nahavandi, Saeid and Khan, Imran and Cheriet, Mohamed, Supervised Class-Pairwise Nmf for Data Representation and Classification. Available at SSRN: https://ssrn.com/abstract=4117410 Rachid Hedjam (Contact Author) Sultan Qaboos University ( email ) PO Box 20Al-khod SQU 123MuscatOman Abdelhamid Abdesselam Sultan Qaboos University ( email ) PO Box 20Al-khod SQU 123MuscatOman Saeid Nahavandi Deakin University ( email ) 75 Pigdons RoadVictoria, 3216Australia Imran Khan Sultan Qaboos University ( email ) PO Box 20Al-khod SQU 123MuscatOman Mohamed Cheriet affiliation not provided to SSRN ( email ) No Address Available Download This Paper Open PDF in Browser Do you want regular updates from SSRN on Twitter? Follow SSRN Paper statistics Downloads 2 Abstract Views 5 PlumX Metrics Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo4 in 0.320 seconds