New approaches to classification and regression for very high dimensional data

Vinod K. Mishra; C.-C. Jay Kuo

doi:10.1117/12.2663378

15 June 2023 New approaches to classification and regression for very high dimensional data

Vinod K. Mishra, C.-C. Jay Kuo

Proceedings Volume 12542, Disruptive Technologies in Information Sciences VII; 125420G (2023) https://doi.org/10.1117/12.2663378
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Feature classification and regression tasks for high dimensional data have been handled by many well-known algorithms like feed-forward multi-level perceptron, decision tree, support vector machine, and many others. Recently, a new approach called subspace learning machine (SLM) has been found which finds a balance between simplicity and effectiveness by partitioning an input feature space into multiple discriminant subspaces in a hierarchical manner. The technique has been extended in many directions to handle high dimensional data. We will emphasize the significance of these developments and present experimental results.

Conference Presentation

Citation Download Citation

Vinod K. Mishra and C.-C. Jay Kuo "New approaches to classification and regression for very high dimensional data", Proc. SPIE 12542, Disruptive Technologies in Information Sciences VII, 125420G (15 June 2023); https://doi.org/10.1117/12.2663378

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available