Many web-based application areas must infer label distributions starting from a small set of sparse, noisy labels. Previous work has shown that graph-based propagation can be very effective at finding the best label distribution across nodes, starting from partial information and a weightedconnection graph. In their work on video recommendations, Baluja et al. showed high-quality results using ...

Introduction. Normalization is an optional step in LCIA that is used to better understand the relative importance and magnitude of the impact category indicator results. It is used for error checking, as a first step in weighting, and for standalone presentation of results. A normalized score for a certain impact category is obtained by determining the ratio of the category indicator result of ...

A BP-based algorithm with 2-dimensional classified normalized correction is developed to reduce the complexity and improve the performance of decoding algorithm for the low density parity check (LDPC) codes. The algorithm first utilizes classification according to the absolute values of incoming messages in check nodes. Then it uses 2-dimensional normalization to correct the minimum and sub-min...

This study focuses on the noise statistics of the normalized data, in time-difference imaging. The commonly practiced normalization procedure, represents the normalized data as the ratio of two random variables. We provide closed form expressions for computing estimates of the first, second order moments, and the covariance matrix of normalized data. The expressions are derived using Taylor ser...

Spatial normalization is an essential preprocessing step in statistical parametric mapping (SPM)-based analysis of PET scans. The standard template provided with the SPM99 software package was originally constructed using (15)O-H(2)O PET scans and is commonly applied regardless of the tracer actually used in the scans being analyzed. This work studies the effect of using three different normali...