Gradient Information for Representation and Modeling
| 
39 | 
36 | 
0
Abstract
Motivated by Fisher divergence, in this paper we present a new set of information quantities which we refer to as gradient information. These measures serve as surrogates for classical information measures such as those based on logarithmic loss, Kullback-Leibler divergence, directed Shannon information, etc. in many data-processing scenarios of interest, and often provide significant computational advantage, improved stability, and robustness. As an example, we apply these measures to the Chow-Liu tree algorithm, and demonstrate remarkable performance and significant computational reduction using both synthetic and real data.
还没有评论，说两句吧！
热门资源
Learning to learn...
The move from hand-designed features to learned...
A Mathematical Mo...
Direct democracy, where each voter casts one vo...
Stratified Strate...
In this paper we introduce Stratified Strategy ...
Rating-Boosted La...
The performance of a recommendation system reli...
Hierarchical Task...
We extend hierarchical task network planning wi...
智能在线
400-630-6780
聆听.建议反馈
E-mail: support@tusaishared.com
资源分类
