"Weighting for more": Enhancing characteristic-function based ICA with asymptotically optimal weighting

Blind Source Separation based on Second-Order Statistics with Asymptotically Optimal Weighting

On weighted local fitting and its relation to the Horvitz-Thompson estimator

Weighting is a largely used concept in many fields of statistics and has frequently caused controversies on its justification and profit. In this paper, we analyze a weighted version of the well-known local polynomial regression estimators, derive their asymptotic bias and variance, and find that the conflict between the asymptotically optimal weighting scheme and the practical requirements has...

Threading without optimizing weighting factors for scoring function.

Optimizing weighting factors for a linear combination of terms in a scoring function is a crucial step for success in developing a threading algorithm. Usually weighting factors are optimized to yield the highest success rate on a training dataset, and the determined constant values for the weighting factors are used for any target sequence. Here we explore completely different approaches to ha...

A Novel Weighting Method Using Fuzzy Set Theory for Spatial Adaptive Patch-based Image Denoising

Based on fuzzy set theory, this paper proposes a novel weighting method for spatial adaptive patch-based image denoising algorithm which can be considered as an extension of nonlocal means filtering. First, a fuzzy clustering algorithm for weighting data points is applied to reduce the estimate bias which arises from the unrelated points. The weighting function is determined by optimal fuzzy pa...

Sequential Weighting Algorithms for Multi-Alphabet Sources∗

The sequential Context Tree Weighting procedure [6] achieves the asymptotically optimal redundancy behavior of k/2 · (log n)/n, where k is the number of free parameters of the source and n is the sequence length. For FSMX sources with an alphabet A, this number, k, is (|A| − 1) · |Ka|. However, especially the FSMX sources often use only a few possible letters in every state. It would be nice if...