Probabilistic Fisher discriminant analysis based on Gaussian mixture model for estimating shale oil sweet spots

2021 ◽  
Author(s):  
Kun Luo ◽  
Zhaoyun Zong
Keyword(s):  
Discriminant Analysis ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Shale Oil ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Sweet Spots

scholarly journals A Bayesian Framework Based on Gaussian Mixture Model and Radial Basis Function Fisher Discriminant Analysis for Flood Spatial Prediction (BayGmmKda V1.1)

2017 ◽  
Author(s):  
Dieu Tien Bui ◽  
Nhat-Duc Hoang
Keyword(s):  
Discriminant Analysis ◽  
Radial Basis Function ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Basis Function ◽  
Gaussian Mixture ◽  
Bayesian Framework ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Radial Basis

Abstract. In this study, a probabilistic model, named as BayGmmKda, is proposed for flood assessment with a study area in Central Vietnam. The new model is essentially a Bayesian framework constructed a combination of Gaussian Mixture Model, Radial Basis Function Fisher Discriminant Analysis, and a Geographic Information System database. Experiments used for measuring the model performance point out that the hybrid framework is superior to other benchmark models including the adaptive neuro fuzzy inference system and the support vector machine. To facility the model implementation, a software program of BayGmmKda has been developed in Matlab environment. The newly proposed model is shown to be a very promising alternative for assisting decision-makers in flood assessment.

Speech Emotion Recognition Using Multiple Discriminant Analysis and Gaussian Mixture Model

2013 ◽  
Vol 380-384 ◽  
pp. 3530-3533
Author(s):  
Yong Qiang Bao ◽  
Li Zhao ◽  
Cheng Wei Huang
Keyword(s):  
Discriminant Analysis ◽  
Emotion Recognition ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Speech Signal ◽  
Recognition Rate ◽  
Gaussian Mixture ◽  
Pitch Contour ◽  
Speech Emotion Recognition ◽  
Multiple Discriminant Analysis

In this paper we studied speech emotion recognition from Mandarin speech signal. Five basic emotion classes and the neutral state are considered. In a listening experiment we verified the speech corpus using a judgment matrix. Acoustic parameters including short-term energy, pitch contour, and formants are extracted from emotional speech signal. Gaussian mixture model is then adopted for training the emotion model. Due to the data challenge in GMM training, we use multiple discriminant analysis for feature optimization and compared with basic Fisher discriminant ratio based method. The experimental results show that using multiple discriminant analysis our GMM classifier gives a promising recognition rate for Mandarin speech emotion recognition.

scholarly journals A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

2017 ◽  
Vol 10 (9) ◽  
pp. 3391-3409 ◽  
Author(s):  
Dieu Tien Bui ◽  
Nhat-Duc Hoang
Keyword(s):  
Land Use ◽  
Discriminant Analysis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Gaussian Mixture ◽  
Bayesian Framework ◽  
Susceptibility Map ◽  
Fisher Discriminant Analysis ◽  
Flood Susceptibility ◽  
Fisher Discriminant

Abstract. In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM), radial-basis-function Fisher discriminant analysis (RBFDA), and a geographic information system (GIS) database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.

FAULT DIAGNOSIS BASED ON MULTI-SCALE CLASSIFICATION USING KERNEL FISHER DISCRIMINANT ANALYSIS AND GAUSSIAN MIXTURE MODEL AND K-NEAREST NEIGHBOR METHOD

2017 ◽  
Vol 79 (5-3) ◽  
Author(s):  
Norazwan Md Nor ◽  
Mohd Azlan Hussain ◽  
Che Rosmani Che Hassan
Keyword(s):  
Fault Diagnosis ◽  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
Gaussian Mixture ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Multi Scale ◽  
Fisher Discriminant ◽  
Kernel Fisher Discriminant Analysis

Effective fault monitoring, detection and diagnosis of chemical processes is important to ensure the consistency and high product quality, as well as the safety of the processes. Fault diagnosis problems can be considered as classification problems as these techniques have been proposed and greatly improved over the past few years. However, a chemical process is often characterized by large scale and non-linear behavior. When linear discriminant analysis is used for fault diagnosis in the system, a lot of incorrect diagnosis will occur. As solution, this paper presents a novel approach for feature extraction and classification framework in chemical process systems based on wavelet transformation and discriminant analysis. The proposed multi-scale kernel Fisher discriminant analysis (MSKFDA) method used the combination of kernel Fisher discriminant analysis (KFDA) and discrete wavelet transform (DWT) to improve the classification performance as compared to conventional approaches. A DWT is applied to extract the process dynamics at different scales by decomposed the data into multiple scales, analyzed by the KFDA and only dynamical characteristics with important information was reconstructed by inverse discrete wavelet transform (IDWT). Then, Gaussian mixture model (GMM) and K-nearest neighbor (KNN) method were individually applied for the fault classification using the output from the MSKFDA approach. These two classifiers are evaluated and compared based on their performance on the Tennessee Eastman process database. The proposed framework for GMM and KNN classifiers had achieved average classification accuracies of 84.72% and 82.00%, respectively, with the results show significant improvement over existing methods in fault detection and classification.

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