A Novel Discriminative Framework Integrating Kernel Entropy Component Analysis and Discriminative Multiple Canonical Correlation for Information Fusion

2016 ◽  
Author(s):  
Lei Gao ◽  
Ling Guan ◽  
Lin Qi ◽  
Enqing Chen
Keyword(s):  
Information Fusion ◽  
Canonical Correlation ◽  
Component Analysis ◽  
Entropy Component

MULTIMODAL INFORMATION FUSION OF AUDIO EMOTION RECOGNITION BASED ON KERNEL ENTROPY COMPONENT ANALYSIS

2013 ◽  
Vol 07 (01) ◽  
pp. 25-42 ◽  
Author(s):  
ZHIBING XIE ◽  
LING GUAN
Keyword(s):  
Emotion Recognition ◽  
Information Fusion ◽  
Feature Space ◽  
Component Analysis ◽  
Information Theoretic ◽  
Second Order Statistics ◽  
New Information ◽  
Multimodal Information Fusion ◽  
Multimodal Information ◽  
Entropy Component

This paper focuses on the application of novel information theoretic tools in the area of information fusion. Feature transformation and fusion is critical for the performance of information fusion, however, the majority of the existing works depend on second order statistics, which is only optimal for Gaussian-like distribution. In this paper, the integration of information fusion techniques and kernel entropy component analysis provides a new information theoretic tool. The fusion of features is realized using descriptor of information entropy and is optimized by entropy estimation. A novel multimodal information fusion strategy of audio emotion recognition based on kernel entropy component analysis (KECA) has been presented. The effectiveness of the proposed solution is evaluated through experimentation on two audiovisual emotion databases. Experimental results show that the proposed solution outperforms the existing methods, especially when the dimension of feature space is substantially reduced. The proposed method offers general theoretical analysis which gives us an approach to implement information theory into multimedia research.

Class-information–incorporated kernel entropy component analysis with application to bearing fault diagnosis

2020 ◽  
pp. 107754632093203
Author(s):  
Hongdi Zhou ◽  
Fei Zhong ◽  
Tielin Shi ◽  
Wuxing Lai ◽  
Jian Duan ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Hebbian Learning ◽  
Component Analysis ◽  
Operating Conditions ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Class Information ◽  
Entropy Component

Rolling bearings are present ubiquitously in industrial fields; timely fault diagnosis is of crucial significance in avoiding serious catastrophe. The extraction of ideal fault feature is a challenging task in vibration-based bearing fault detection. In this article, a novel method called class-information–incorporated kernel entropy component analysis is proposed for bearing fault diagnosis. The method is developed based on the Hebbian learning theory of neural network and the kernel entropy component analysis which attempts to compress the most Renyi quadratic entropy of input dataset after dimension reduction and presents a good performance for nonlinear feature extraction. Class-information–incorporated kernel entropy component analysis can take advantage of the label information of training samples to guide dimensional reduction and still follow the same simple mathematical formulation as kernel entropy component analysis. The high-dimensional feature dataset including time-domain, frequency-domain, and time–frequency domain characteristic parameters is first derived from the vibration signals. Then, the intrinsic geometric features are extracted by class-information–incorporated kernel entropy component analysis, and a classification strategy based on fusion information is applied to recognize different operating conditions of bearings. The experimental results demonstrated the feasibility and effectiveness of the proposed method.

scholarly journals Kernel Entropy Component Analysis for Remote Sensing Image Clustering

2012 ◽  
Vol 9 (2) ◽  
pp. 312-316 ◽  
Author(s):  
Luis Gomez-Chova ◽  
Robert Jenssen ◽  
Gustavo Camps-Valls
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Component Analysis ◽  
Image Clustering ◽  
Entropy Component
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