Kernel optimization using conformal maps for the minimal complexity machine. (November 2021)

• Record Type: Journal Article
• Authors: Badge, Skyler; Soman, Sumit; Chandra, Suresh; Jayadeva,
• Is Part Of: Engineering applications of artificial intelligence. Volume 106(2021)
• Publication Date: 2021-11
• Subjects: Minimal complexity machine -- Kernel optimization -- Conformal kernel -- Generalized eigenvalue problem -- Support vector machines; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104493
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: The Minimal Complexity Machine (MCM) is a kernel-based learning model that can learn very sparse models that yield comparable or better performance than Support Vector Machines (SVMs). However, kernel optimization for the MCM has not yet been explored. It has been shown in prior work that a data dependent kernel helps improve generalization. We show results on data dependent optimized kernels for the MCM and a large-scale MCM variant. Results on benchmark datasets demonstrate both model sparsity and improved generalization.