کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
410150 679124 2013 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Regularized least squares fisher linear discriminant with applications to image recognition
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Regularized least squares fisher linear discriminant with applications to image recognition
چکیده انگلیسی

Recursive concave-convex Fisher Linear Discriminant (RPFLD) is a novel efficient dimension reduction method and has been successfully applied to image recognition. However, RPFLD suffers from singularity problem and may lose some useful discriminant information when applied to high-dimensional data. Moreover, RPFLD is computationally expensive because it has to solve a series of quadratic programming (QP) problems to obtain optimal solution. In order to improve the generalization performance of RPFLD and at the same time reduce its training burden, we propose a novel method termed as regularized least squares Fisher linear discriminant (RLS-FLD) in this paper. The central idea is to introduce regularization into RPFLD and simultaneously use the 2-norm loss function. In doing so, the objective function of RLS-FLD turns out to be positive-definite, thus avoiding singularity problem. To solve RLS-FLD, the concave-convex programming (CCP) algorithm is employed to convert the original nonconvex problem to a series of equality-constrained convex QP problems. Each optimization problem in this series has a closed-form solution in its primal formulation via classic Lagrangian method. The resulting RLS-FLD thus leads to much fast training speed and does not need any optimization packages. Meanwhile, theoretical analysis is provided to uncover the connections between RLS-FLD and regularized linear discriminant analysis (RLDA), thus giving more insight into the principle of RLS-FLD. The effectiveness of the proposed RLS-FLD is demonstrated by experimental results on some real-world handwritten digit, face and object recognition datasets.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 122, 25 December 2013, Pages 521–534
نویسندگان
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