Orthogonal vs. uncorrelated least squares discriminant analysis for feature extraction

• المؤلفون الرئيسيون: Nie, Feiping, Xiang, Shiming, Liu, Yun, Hou, Chenping, Zhang, Changshui
• مؤلفون آخرون: School of Electrical and Electronic Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2013
• الموضوعات: DRNTU::Engineering::Electrical and electronic engineering
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/105687; http://hdl.handle.net/10220/17576; http://dx.doi.org/10.1016/j.patrec.2011.11.028
• المؤسسة: Nanyang Technological University

In this paper, a new discriminant analysis for feature extraction is derived from the perspective of least squares regression. To obtain great discriminative power between classes, all the data points in each class are expected to be regressed to a single vector, and the basic task is to find a tran...