scholarly journals A new approach to mutual information. II

2010 ◽  
Author(s):  
Fumio Hiai ◽  
Takuho Miyamoto
Keyword(s):  
Mutual Information ◽  
New Approach

Blind frequency deconvolution : A new approach using mutual information rate

2004 ◽  
Author(s):  
Anthony Larue ◽  
Jéro⁁me I. Mars ◽  
Christian Jutten
Keyword(s):  
Mutual Information ◽  
Information Rate ◽  
New Approach

New approach for studies of time series of satellite observations of rainstorms: II. The mutual information dimensions

2004 ◽  
Author(s):  
Lisheng Xu ◽  
Yongzhong Wang ◽  
Jilie Ding ◽  
Andrew Y. S. Cheng ◽  
Xiuwan Chen ◽  
...  
Keyword(s):  
Time Series ◽  
Mutual Information ◽  
Satellite Observations ◽  
New Approach

scholarly journals A new approach to mutual information

2007 ◽  
Author(s):  
Fumio Hiai ◽  
Dénes Petz
Keyword(s):  
Mutual Information ◽  
New Approach

The Correlation Analysis of Discrete Variables and Continuous Variables Based on Mutual Information

2011 ◽  
Vol 121-126 ◽  
pp. 4203-4207 ◽  
Author(s):  
Lin Huo ◽  
Chuan Lv ◽  
Si Miao Fei ◽  
Dong Zhou
Keyword(s):  
Control System ◽  
Mutual Information ◽  
Correlation Analysis ◽  
Continuous Variables ◽  
Discrete Variables ◽  
Fire Control ◽  
New Approach ◽  
Information Method ◽  
Monitor Data

As most Mutual Information method is limited to the correlation analysis between discrete variables in majority and tendency of choosing the characteristic variables with multi-values so far, in this paper we propose a new approach based on Mutual Information to measure the correlation of discrete variables and continuous variables. Then we take the fire control system of aircraft for example to calculate the correlation between fault types and monitor data indexes, and finally find the fault symptom classes.

Part Recognition Based on Maximum Mutual Information

2009 ◽  
Vol 407-408 ◽  
pp. 234-238
Author(s):  
Sen Ge ◽  
Da Gui Huang
Keyword(s):  
Mutual Information ◽  
Image Recognition ◽  
Spatial Information ◽  
Image Feature ◽  
Shape Information ◽  
Translation Invariant ◽  
Matching Criterion ◽  
Local Shape ◽  
New Approach ◽  
Maximum Mutual Information

A new approach to the problem of part recognition is proposed by using maximum mutual information. The method applies entropy to measure image feature, combined with color information and local shape information, and uses mutual information as a new matching criterion between the images for image recognition. This method solves the problem that histogram algorithm can not represent the spatial information. This method not only has the feature of translation invariant, but also avoids image segmentation which may lead to a complex calculation, so it can be realized easily. The result shows that proposed approach is accuracy, stability, and reliability in the processing of machine part image recognition.

Constrained Subspace ICA Based on Mutual Information Optimization Directly

2008 ◽  
Vol 20 (4) ◽  
pp. 964-973 ◽  
Author(s):  
Marc M. Van Hulle
Keyword(s):  
Mutual Information ◽  
Independent Component Analysis ◽  
Gradient Descent ◽  
Edgeworth Expansion ◽  
Component Analysis ◽  
Independent Component ◽  
New Approach ◽  
Robust Version ◽  
Constrained Independent Component Analysis ◽  
Information Optimization

We introduce a new approach to constrained independent component analysis (ICA) by formulating the original, unconstrained ICA problem as well as the constraints in mutual information terms directly. As an estimate of mutual information, a robust version of the Edgeworth expansion is used, on which gradient descent is performed. As an application, we consider the extraction of both the mother and the fetal antepartum electrocardiograms (ECG) from multielectrode cutaneous recordings on the mother's thorax and abdomen.

scholarly journals Matrix Liberation Process II: Relation to Orbital Free Entropy

2020 ◽  
pp. 1-49
Author(s):  
Yoshimichi Ueda
Keyword(s):  
Mutual Information ◽  
Upper Bound ◽  
Large Deviation Principle ◽  
Large Deviation ◽  
Rate Function ◽  
New Approach ◽  
Free Entropy ◽  
The Matrix ◽  
Orbital Free ◽  
Definition Of

Abstract We investigate the concept of orbital free entropy from the viewpoint of the matrix liberation process. We will show that many basic questions around the definition of orbital free entropy are reduced to the question of full large deviation principle for the matrix liberation process. We will also obtain a large deviation upper bound for a certain family of random matrices that is essential to define the orbital free entropy. The resulting rate function is made up into a new approach to free mutual information.

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