An Adaptive Ridge Procedure for L0 Regularization
نویسندگان
چکیده
منابع مشابه
An Adaptive Ridge Procedure for L0 Regularization
Penalized selection criteria like AIC or BIC are among the most popular methods for variable selection. Their theoretical properties have been studied intensively and are well understood, but making use of them in case of high-dimensional data is difficult due to the non-convex optimization problem induced by L0 penalties. In this paper we introduce an adaptive ridge procedure (AR), where itera...
متن کاملL0 Regularization
We propose a practical method for L0 norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularization is interesting since (1) it can greatly speed up training and inference, and (2) it can improve generalization. AIC and BIC, well-known model selection criteria, are special cases of L0 regularization. However, since...
متن کاملLearning Sparse Neural Networks through L0 Regularization
We propose a practical method for L0 norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularization is interesting since (1) it can greatly speed up training and inference, and (2) it can improve generalization. AIC and BIC, well-known model selection criteria, are special cases of L0 regularization. However, since...
متن کاملBi-l0-l2-Norm Regularization for Blind Motion Deblurring
In blind motion deblurring, leading methods today tend towards highly non-convex approximations of the l0-norm, especially in the image regularization term. In this paper, we propose a simple, effective and fast approach for the estimation of the motion blur-kernel, through a bi-l0-l2-norm regularization imposed on both the intermediate sharp image and the blur-kernel. Compared with existing me...
متن کاملRidge-Penalty Regularization for Kernel-CCA
CCA and Kernel-CCA are powerful statistical tools that have been successfully employed for feature extraction. However, when working in high-dimensional signal spaces, care has to be taken to avoid overfitting. This paper discusses the influence of ridge penalty regularization on kernel-CCA by relating it to multivariate linear regression(MLR) and partial least squares(PLS). Experimental result...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLOS ONE
سال: 2016
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0148620