Feature extraction for multimodal biometric and study of fusion using Gaussian mixture model

2012 ◽  
Author(s):  
S. Arun Vivek ◽  
J. Aravinth ◽  
S. Valarmathy
Keyword(s):  
Feature Extraction ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture

Adaptive Gaussian Mixture Model and Convolution Autoencoder Clustering for Unsupervised Seismic Waveform Analysis

2021 ◽  
pp. 1-46
Author(s):  
Donglin Zhu ◽  
Jingbin Cui ◽  
Yan Li ◽  
Zhonghong Wan ◽  
Lei Li
Keyword(s):  
Feature Extraction ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Facies Analysis ◽  
Gaussian Mixture ◽  
Seismic Facies ◽  
Waveform Analysis ◽  
Western China ◽  
Reservoir Properties ◽  
Seismic Waveform

Seismic facies analysis can effectively estimate reservoir properties and seismic waveform clustering is a useful tool for facies analysis. We developed a deep learning-based clustering approach called the modified deep convolutional embedded clustering with adaptive Gaussian mixture model (AGMM-MDCEC) for seismic waveform clustering. Trainable feature extraction and clustering layers in AGMM-MDCEC are implemented using neural networks. The two independent processes of feature extraction and clustering are fused, such that extracted features are modified simultaneously with the results of clustering. A convolutional autoencoder is used in the algorithm for extracting features from seismic data and reduce data redundancy. At the same time, weights of clustering network are fined-tuned through iteration to obtain state-of-the-art clustering results. We apply our new classification algorithm to a data volume acquired in western China to map architectural elements of a complex fluvial depositional system. Our proposed method obtains superior results over those provided by traditional K-means, Gaussian mixture model, and some machine learning methods, and improves the mapping of the extent of the distributary system.

Gaussian mixture model-based target feature extraction and visualization

2021 ◽  
Author(s):  
Ji Ma ◽  
Jinjin Chen ◽  
Liye Chen ◽  
Xingjian Zhou ◽  
Xujia Qin ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Target Feature ◽  
Model Based

scholarly journals A Tandem Feature Extraction Approach for Arrhythmia Identification

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 976
Author(s):  
Javier Tejedor ◽  
David G. Marquez ◽  
Constantino A. Garcia ◽  
Abraham Otero
Keyword(s):  
Feature Extraction ◽  
Heart Disease ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Feature Vector ◽  
Gaussian Mixture ◽  
Hermite Functions ◽  
Identification System ◽  
Feature Vectors ◽  
Original Feature

Heart disease is currently the leading cause of death in the world. The electrocardiogram (ECG) is the recording of the electrical activity generated by the heart. Its low cost and simplicity have made it an essential test for monitoring heart disease, especially for the identification of arrhythmias. With the advances in electronic technology, there are nowadays sensors that enable the recording of the ECG during the daily life of the patient and its wireless transmission to healthcare facilities. This type of information has a great potential to detect cardiac diseases in their early stages and to permit early interventions before the patient’s health deteriorates. However, to usefully exploit the large volume of information obtained from ambulatory ECG, pattern recognition techniques that are capable of automatically analyzing it are required. Tandem feature extraction techniques have proven to be useful for the processing of physiological parameters such as the electroencephalogram (EEG) and speech. However, to the best of our knowledge, they have never been applied to the ECG. In this paper, the utility of tandem feature extraction for the identification of arrhythmias is studied. The coefficients of a regression using Hermite functions are used to create a feature vector that represents the heartbeat. A multiple-layer perceptron (MLP) is trained using these features and its posterior probability outputs are used to extend the original feature vector. Finally, a Gaussian mixture model (GMM) is trained on the extended feature vectors, which is then used in a GMM-based arrhythmia identification system. This approach has been validated using the MIT-BIH Arrhythmia database. The accuracy of the Gaussian mixture model increased by 15.8% when applied over the extended feature vectors, compared to its application over the original feature vectors, showing the potential of tandem feature extraction for ECG analysis and arrhythmia identification.

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