A branch and bound method solving the max–min linear discriminant analysis problem

نویسندگان

چکیده

Fisher linear discriminant analysis (FLDA or LDA) is a well-known technique for dimension reduction and classification. The method was first formulated in 1936 by the one-dimensional setting. In this paper, we will examine LDA problem using different objective function. Instead of maximizing sum all distances between classes, define an function that maximize minimum separation among classes. This leads to difficult nonconvex optimization problem. We present branch bound case where space.

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ژورنال

عنوان ژورنال: Optimization Methods & Software

سال: 2023

ISSN: ['1055-6788', '1026-7670', '1029-4937']

DOI: https://doi.org/10.1080/10556788.2023.2198769