Online Multibody Factorization Based on Bayesian Principal Component Analysis of Gaussian Mixture Models

2009 ◽  
pp. 679-687
Author(s):  
Kentarou Hitomi ◽  
Takashi Bando ◽  
Naoki Fukaya ◽  
Kazushi Ikeda ◽  
Tomohiro Shibata
Keyword(s):  
Principal Component Analysis ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Principal Component ◽  
Component Analysis ◽  
Gaussian Mixture

Probability Estimation of Direct Hydrocarbon Indicators Using Gaussian Mixture Models

2021 ◽  
Author(s):  
John B. Lemos ◽  
Matheus R. S. Barbosa ◽  
Edric B. Troccoli ◽  
Alexsandro G. Cerqueira
Keyword(s):  
Cluster Analysis ◽  
Mixture Models ◽  
Clustering Algorithm ◽  
Gaussian Mixture Models ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Optimal Number ◽  
Original Dataset ◽  
Pca Algorithm ◽  
Optimal Number Of Clusters

This work aims to delimit the Direct Hydrocarbon Indicators (DHI) zones using the Gaussian Mixture Models (GMM) algorithm, an unsupervised machine learning method, over the FS8 seismic horizon in the seismic data of the Dutch F3 Field. The dataset used to perform the cluster analysis was extracted from the 3D seismic dataset. It comprises the following seismic attributes: Sweetness, Spectral Decomposition, Acoustic Impedance, Coherence, and Instantaneous Amplitude. The Principal Component Analysis (PCA) algorithm was applied in the original dataset for dimensionality reduction and noise filtering, and we choose the first three principal components to be the input of the clustering algorithm. The cluster analysis using the Gaussian Mixture Models was performed by varying the number of groups from 2 to 20. The Elbow Method suggested a smaller number of groups than needed to isolate the DHI zones. Therefore, we observed that four is the optimal number of clusters to highlight this seismic feature. Furthermore, it was possible to interpret other clusters related to the lithology through geophysical well log data.

SPEAKER IDENTIFICATION BY AGGREGATING GAUSSIAN MIXTURE MODELS (GMMs) BASED ON UNCORRELATED MFCC-DERIVED FEATURES

2014 ◽  
Vol 28 (04) ◽  
pp. 1456006 ◽  
Author(s):  
AMITA PAL ◽  
SMARAJIT BOSE ◽  
GOPAL K. BASAK ◽  
AMITAVA MUKHOPADHYAY
Keyword(s):  
Mixture Models ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Gaussian Mixture Models ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Mel Frequency Cepstral Coefficients ◽  
Speech Corpus ◽  
Signal Process

For solving speaker identification problems, the approach proposed by Reynolds [IEEE Signal Process. Lett.2 (1995) 46–48], using Gaussian Mixture Models (GMMs) based on Mel Frequency Cepstral Coefficients (MFCCs) as features, is one of the most effective available in the literature. The use of GMMs for modeling speaker identity is motivated by the interpretation that the Gaussian components represent some general speaker-dependent spectral shapes, and also by the capability of Gaussian mixtures to model arbitrary densities. In this work, we have initially illustrated, with the help of a new bilingual speech corpus, how the well-known principal component transformation, in conjunction with the principle of classifier combination can be used to enhance the performance of the MFCC-GMM speaker recognition systems significantly. Subsequently, we have emphatically and rigorously established the same using the benchmark speech corpus NTIMIT. A significant outcome of this work is that the proposed approach has the potential to enhance the performance of any speaker recognition system based on correlated features.

scholarly journals Resident user load classification method based on improved Gaussian mixture model clustering

2022 ◽  
Vol 355 ◽  
pp. 02024
Author(s):  
Haojing Wang ◽  
Yingjie Tian ◽  
An Li ◽  
Jihai Wu ◽  
Gaiping Sun
Keyword(s):  
Principal Component Analysis ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Clustering Algorithm ◽  
Computational Cost ◽  
Principal Component ◽  
Component Analysis ◽  
Gaussian Mixture ◽  
Classification Method ◽  
Mixture Model Clustering

In view of the limitation of “hard assignment” of clusters in traditional clustering methods and the difficulty of meeting the requirements of clustering efficiency and clustering accuracy simultaneously in regard to massive data sets, a load classification method based on a Gaussian mixture model combining clustering and principal component analysis is proposed. The load data are fed into a Gaussian mixture model clustering algorithm after principal component analysis and dimensionality reduction to achieve classification of large-scale load datasets. The method in this paper is used to classify loads in the Canadian AMPds2 public dataset and is compared with K-Means, Gaussian mixed model clustering and other methods. The results show that the proposed method can not only achieve load classification more effectively and finely, but also save computational cost and improve computational efficiency.

Detection of tidal tails around the open cluster M 67 using principal component analysis

2020 ◽  
Vol 72 (3) ◽  
Author(s):  
Xinhua Gao
Keyword(s):  
Principal Component Analysis ◽  
Loss Rate ◽  
Mass Loss Rate ◽  
Open Cluster ◽  
Principal Component ◽  
Mass Range ◽  
Component Analysis ◽  
Gaussian Mixture ◽  
Tidal Tails ◽  
Gaia Dr2

Abstract In this paper we present an investigation of tidal tails around the old open cluster M 67 using Gaia-DR2. We identify likely extra-tidal stars around M 67 using principal component analysis (PCA) and a Gaussian mixture model (GMM). We find 1618 stars closely related to M 67, 85 of which are likely extra-tidal stars. We find clear evidence for the existence of two well-defined tidal tails emerging from M 67. The tidal tails extend out to projected distances of at least ${2{^{\circ}_{.}}5}$ (∼39 pc), which is more than twice as large as the tidal radius of the cluster. Based on LAMOST-DR5 data, we confirm that 13 extra-tidal stars have radial velocities and metallicities similar to those of the cluster. Furthermore, we also confirm that the extra-tidal stars cover a wide mass range of 0.2–1.1 M⊙, and nearly half the extra-tidal stars are less than 0.57 M⊙. The total mass of the extra-tidal stars is determined to be about 55 M⊙. We estimate a mass-loss rate of ∼2.8 M⊙ Myr−1 for M 67. Possible origins of these extra-tidal stars are discussed.

Application of Kernel Trick to Fuzzy c-Means with Regularization by K-L Information

2004 ◽  
Vol 8 (6) ◽  
pp. 566-572 ◽  
Author(s):  
Hidetomo Ichihashi ◽  
◽  
Katsuhiro Honda
Keyword(s):  
Principal Component Analysis ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Gaussian Mixture ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Fisher Discriminant Analysis ◽  
Kernel Trick ◽  
Kernel Approach

Support vector machines (SVM), kernel principal component analysis (KPCA), and kernel Fisher discriminant analysis (KFD), are examples of successful kernel-based learning methods. By the addition of a regularizer and the kernel trick to a fuzzy counterpart of Gaussian mixture models (GMM), this paper proposes a clustering algorithm in an extended high dimensional feature space. Unlike the global nonlinear approaches, GMM or its fuzzy counterpart is to model nonlinear structure with a collection, or mixture, of local linear sub-models of PCA. When the number of feature vectors and clusters are n and c respectively, this kernel approach can find up to c × n nonzero eigenvalues. A way to control the number of parameters in the mixture of probabilistic principal component analysis (PPCA) is adopted to reduce the number of parameters. The algorithm provides a partitioning with flexible shape of clusters in the original input data space.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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