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Journal articles on the topic 'Gaussian Mixture Model'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 January 2025

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1

Zickert, Gustav, and Can Evren Yarman. "Gaussian mixture model decomposition of multivariate signals." Signal, Image and Video Processing 16, no. 2 (October 29, 2021): 429–36. http://dx.doi.org/10.1007/s11760-021-01961-y.

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AbstractWe propose a greedy variational method for decomposing a non-negative multivariate signal as a weighted sum of Gaussians, which, borrowing the terminology from statistics, we refer to as a Gaussian mixture model. Notably, our method has the following features: (1) It accepts multivariate signals, i.e., sampled multivariate functions, histograms, time series, images, etc., as input. (2) The method can handle general (i.e., ellipsoidal) Gaussians. (3) No prior assumption on the number of mixture components is needed. To the best of our knowledge, no previous method for Gaussian mixture model decomposition simultaneously enjoys all these features. We also prove an upper bound, which cannot be improved by a global constant, for the distance from any mode of a Gaussian mixture model to the set of corresponding means. For mixtures of spherical Gaussians with common variance $$\sigma ^2$$ σ 2 , the bound takes the simple form $$\sqrt{n}\sigma $$ n σ . We evaluate our method on one- and two-dimensional signals. Finally, we discuss the relation between clustering and signal decomposition, and compare our method to the baseline expectation maximization algorithm.

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2

MA, JINWEN, and TAIJUN WANG. "ENTROPY PENALIZED AUTOMATED MODEL SELECTION ON GAUSSIAN MIXTURE." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 08 (December 2004): 1501–12. http://dx.doi.org/10.1142/s0218001404003812.

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Gaussian mixture modeling is a powerful approach for data analysis and the determination of the number of Gaussians, or clusters, is actually the problem of Gaussian mixture model selection which has been investigated from several respects. This paper proposes a new kind of automated model selection algorithm for Gaussian mixture modeling via an entropy penalized maximum-likelihood estimation. It is demonstrated by the experiments that the proposed algorithm can make model selection automatically during the parameter estimation, with the mixing proportions of the extra Gaussians attenuating to zero. As compared with the BYY automated model selection algorithms, it converges more stably and accurately as the number of samples becomes large.

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3

Mirra, J., and S. Abdullah. "Bayesian gaussian finite mixture model." Journal of Physics: Conference Series 1725 (January 2021): 012084. http://dx.doi.org/10.1088/1742-6596/1725/1/012084.

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4

Wichert, Andreas. "Quantum-like Gaussian mixture model." Soft Computing 25, no. 15 (June 11, 2021): 10067–81. http://dx.doi.org/10.1007/s00500-021-05941-9.

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5

Lotsi, Anani, and Ernst Wit. "Sparse Gaussian graphical mixture model." Afrika Statistika 11, no. 2 (December 1, 2016): 1041–59. http://dx.doi.org/10.16929/as/2016.1041.91.

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6

Nguyen, Thanh Minh, Q. M. Jonathan Wu, and Hui Zhang. "Bounded generalized Gaussian mixture model." Pattern Recognition 47, no. 9 (September 2014): 3132–42. http://dx.doi.org/10.1016/j.patcog.2014.03.030.

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7

Xie, Fangzheng, and Yanxun Xu. "Bayesian Repulsive Gaussian Mixture Model." Journal of the American Statistical Association 115, no. 529 (April 1, 2019): 187–203. http://dx.doi.org/10.1080/01621459.2018.1537918.

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8

Alangari, Nourah, Mohamed El Bachir Menai, Hassan Mathkour, and Ibrahim Almosallam. "Intrinsically Interpretable Gaussian Mixture Model." Information 14, no. 3 (March 3, 2023): 164. http://dx.doi.org/10.3390/info14030164.

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Understanding the reasoning behind a predictive model’s decision is an important and longstanding problem driven by ethical and legal considerations. Most recent research has focused on the interpretability of supervised models, whereas unsupervised learning has received less attention. However, the majority of the focus was on interpreting the whole model in a manner that undermined accuracy or model assumptions, while local interpretation received much less attention. Therefore, we propose an intrinsic interpretation for the Gaussian mixture model that provides both global insight and local interpretations. We employed the Bhattacharyya coefficient to measure the overlap and divergence across clusters to provide a global interpretation in terms of the differences and similarities between the clusters. By analyzing the GMM exponent with the Garthwaite–Kock corr-max transformation, the local interpretation is provided in terms of the relative contribution of each feature to the overall distance. Experimental results obtained on three datasets show that the proposed interpretation method outperforms the post hoc model-agnostic LIME in determining the feature contribution to the cluster assignment.

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9

Kim, Sung-Suk, Keun-Chang Kwak, Jeong-Woong Ryu, and Myung-Geun Chun. "A Neuro-Fuzzy Modeling using the Hierarchical Clustering and Gaussian Mixture Model." Journal of Korean Institute of Intelligent Systems 13, no. 5 (October 1, 2003): 512–19. http://dx.doi.org/10.5391/jkiis.2003.13.5.512.

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10

Wei, Hui, and Wei Zheng. "Image Denoising Based on Improved Gaussian Mixture Model." Scientific Programming 2021 (September 22, 2021): 1–8. http://dx.doi.org/10.1155/2021/7982645.

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An image denoising method is proposed based on the improved Gaussian mixture model to reduce the noises and enhance the image quality. Unlike the traditional image denoising methods, the proposed method models the pixel information in the neighborhood around each pixel in the image. The Gaussian mixture model is employed to measure the similarity between pixels by calculating the L2 norm between the Gaussian mixture models corresponding to the two pixels. The Gaussian mixture model can model the statistical information such as the mean and variance of the pixel information in the image area. The L2 norm between the two Gaussian mixture models represents the difference in the local grayscale intensity and the richness of the details of the pixel information around the two pixels. In this sense, the L2 norm between Gaussian mixture models can more accurately measure the similarity between pixels. The experimental results show that the proposed method can improve the denoising performance of the images while retaining the detailed information of the image.

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11

Liu, Jialu, Deng Cai, and Xiaofei He. "Gaussian Mixture Model with Local Consistency." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 3, 2010): 512–17. http://dx.doi.org/10.1609/aaai.v24i1.7659.

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Gaussian Mixture Model (GMM) is one of the most popular data clustering methods which can be viewed as a linear combination of different Gaussian components. In GMM, each cluster obeys Gaussian distribution and the task of clustering is to group observations into different components through estimating each cluster's own parameters. The Expectation-Maximization algorithm is always involved in such estimation problem. However, many previous studies have shown naturally occurring data may reside on or close to an underlying submanifold. In this paper, we consider the case where the probability distribution is supported on a submanifold of the ambient space. We take into account the smoothness of the conditional probability distribution along the geodesics of data manifold. That is, if two observations are close in intrinsic geometry, their distributions over different Gaussian components are similar. Simply speaking, we introduce a novel method based on manifold structure for data clustering, called Locally Consistent Gaussian Mixture Model (LCGMM). Specifically, we construct a nearest neighbor graph and adopt Kullback-Leibler Divergence as the distance measurement to regularize the objective function of GMM. Experiments on several data sets demonstrate the effectiveness of such regularization.

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12

Lakshmanan, Valliappa, and John S. Kain. "A Gaussian Mixture Model Approach to Forecast Verification." Weather and Forecasting 25, no. 3 (June 1, 2010): 908–20. http://dx.doi.org/10.1175/2010waf2222355.1.

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Abstract Verification methods for high-resolution forecasts have been based either on filtering or on objects created by thresholding the images. The filtering methods do not easily permit the use of deformation while identifying objects based on thresholds can be problematic. In this paper, a new approach is introduced in which the observed and forecast fields are broken down into a mixture of Gaussians, and the parameters of the Gaussian mixture model fit are examined to identify translation, rotation, and scaling errors. The advantages of this method are discussed in terms of the traditional filtering or object-based methods and the resulting scores are interpreted on a standard verification dataset.

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13

Sun, Tongjing, Yabin Wen, Xuegang Zhang, Bing Jia, and Mengwei Zhou. "Gaussian Mixture Model for Marine Reverberations." Applied Sciences 13, no. 21 (November 6, 2023): 12063. http://dx.doi.org/10.3390/app132112063.

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Ocean reverberations, a significant interference source in active sonar, arise as a response generated by random scattering at the receiving end, a consequence of randomly distributed clutter or irregular interfaces. Statistical analysis of reverberation data has revealed a predominant adherence to the Rayleigh distribution, signifying its departure from specific distribution forms like the Gaussian distribution. This study introduces the Gaussian mixture model, capable of simulating random variables conforming to a wide array of distributions through the integration of an adequate number of components. Leveraging the unique statistical attributes of reverberation, we initiate the Gaussian mixture model’s parameters via the frequency histogram of the reverberation data. Subsequently, model parameters are estimated using the expectation–maximization (EM) algorithm and the most suitable statistical model is selected based on robust model selection criteria. Through a comprehensive evaluation that encompasses both simulated and observed data, our results underscore the Gaussian mixture model’s effectiveness in accurately characterizing the distribution of reverberation data, yielding a mean squared error of less than 4‰.

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14

McNicholas, Paul D. "Model-based classification using latent Gaussian mixture models." Journal of Statistical Planning and Inference 140, no. 5 (May 2010): 1175–81. http://dx.doi.org/10.1016/j.jspi.2009.11.006.

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15

Ban, Zhihua, Jianguo Liu, and Li Cao. "Superpixel Segmentation Using Gaussian Mixture Model." IEEE Transactions on Image Processing 27, no. 8 (August 2018): 4105–17. http://dx.doi.org/10.1109/tip.2018.2836306.

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16

Amreen, H. Aaliya, and K. Khadar Nawas. "Speaker Recognition using Gaussian Mixture Model." International Journal of Computer & Organization Trends 33, no. 1 (July 25, 2016): 45–50. http://dx.doi.org/10.14445/22492593/ijcot-v33p310.

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17

Koppen, Paul, Zhen-Hua Feng, Josef Kittler, Muhammad Awais, William Christmas, Xiao-Jun Wu, and He-Feng Yin. "Gaussian mixture 3D morphable face model." Pattern Recognition 74 (February 2018): 617–28. http://dx.doi.org/10.1016/j.patcog.2017.09.006.

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18

Chen, Zezhi, and Tim Ellis. "A self-adaptive Gaussian mixture model." Computer Vision and Image Understanding 122 (May 2014): 35–46. http://dx.doi.org/10.1016/j.cviu.2014.01.004.

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19

Ma, Jiyong, and Wen Gao. "The supervised learning Gaussian mixture model." Journal of Computer Science and Technology 13, no. 5 (September 1998): 471–74. http://dx.doi.org/10.1007/bf02948506.

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20

Pinto, Rafael Coimbra, and Paulo Martins Engel. "A Fast Incremental Gaussian Mixture Model." PLOS ONE 10, no. 10 (October 7, 2015): e0139931. http://dx.doi.org/10.1371/journal.pone.0139931.

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21

Abdoli, Mohsen, Mohammad Ghanbari, Hossein Sarikhani, and Patrice Brault. "Gaussian mixture model-based contrast enhancement." IET Image Processing 9, no. 7 (July 1, 2015): 569–77. http://dx.doi.org/10.1049/iet-ipr.2014.0583.

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22

Giuliani, Donatella. "A Grayscale Segmentation Approach Using the Firefly Algorithm and the Gaussian Mixture Model." International Journal of Swarm Intelligence Research 9, no. 1 (January 2018): 39–57. http://dx.doi.org/10.4018/ijsir.2018010103.

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In this article, the author proposes an unsupervised grayscale image segmentation method based on a combination of the Firefly Algorithm and the Gaussian Mixture Model. Firstly, the Firefly Algorithm has been applied in a histogram-based research of cluster centroids. The Firefly Algorithm is a stochastic global optimization technique, centred on the flashing characteristics of fireflies. In this histogram-based segmentation approach, it is employed to determine the number of clusters and to select the gray levels for grouping pixels into homogeneous regions. Successively these gray values are used in the initialization step for the parameter estimation of a Gaussian Mixture Model. The parametric probability density function of a Gaussian Mixture Model is represented as a weighted sum of Gaussian components, whose parameters are evaluated applying the iterative Expectation-Maximization technique. The coefficients of the linear super-position of Gaussians can be thought as prior probabilities of each component. Applying the Bayes rule, the posterior probabilities of the grayscale intensities have been evaluated, therefore their maxima are used to assign each pixel to the clusters, according to their gray levels.

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23

Kawabata, Takeshi. "Gaussian-input Gaussian mixture model for representing density maps and atomic models." Journal of Structural Biology 203, no. 1 (July 2018): 1–16. http://dx.doi.org/10.1016/j.jsb.2018.03.002.

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24

Gupta, Shaveta, and Vinay Bhatia. "GMMC: Gaussian Mixture Model Based Clustering Hierarchy Protocol in Wireless Sensor Network." International Journal of Scientific Engineering and Research 3, no. 7 (July 27, 2015): 44–49. https://doi.org/10.70729/ijser15325.

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25

Huang, Jin, Wei Dong Jin, and Na Qin. "Moving Objects Detection Algorithm Based on Three-Dimensional Gaussian Mixture Codebook Model Using XYZ Color Model." Applied Mechanics and Materials 347-350 (August 2013): 3505–9. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3505.

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In order to reduce the difficulty of adjusting parameters for the codebook model and the computational complexity of probability distribution for the Gaussian mixture model in intelligent visual surveillance, a moving objects detection algorithm based on three-dimensional Gaussian mixture codebook model using XYZ color model is proposed. In this algorithm, a codebook model based on XYZ color model is built, and then the Gaussian model based on X, Y and Z components in codewords is established respectively. In this way, the characteristic of the three-dimensional Gaussian mixture model for the codebook model is obtained. The experimental results show that the proposed algorithm can attain higher real-time capability and its average frame rate is about 16.7 frames per second, while it is about 8.3 frames per second for the iGMM (improved Gaussian mixture model) algorithm, about 6.1 frames per second for the BM (Bayes model) algorithm, about 12.5 frames per second for the GCBM (Gaussian-based codebook model) algorithm, and about 8.5 frames per second for the CBM (codebook model) algorithm in the comparative experiments. Furthermore the proposed algorithm can obtain better detection quantity.

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26

Wu, Xin Ping, and Min Cang Fu. "Moving Target Detection Based on Double Model." Advanced Materials Research 998-999 (July 2014): 759–62. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.759.

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In order to overcome the problem that the single-Gauss model is poor of anti-interference and Gaussian Mixture model is poor of real-time, we present the double modeling algorithm of moving target detection. We use three frame difference method to distinguish the invariant region and complex region in background. And then we use single-Gauss modeling to model the invariant background while the complex region of background would be modeled with Gaussian Mixture modeling. It is more effective than the single-Gauss model and more efficient than the Gaussian Mixture model .The experimental results show that the improved algorithm is superior to the traditional single Gauss model or Gaussian Mixture model. It can detect moving target more quickly and accurately, with good robustness and real-time.

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27

Xia, Rui, Qiuyue Zhang, and Xiaoyan Deng. "Multiscale Gaussian convolution algorithm for estimate of Gaussian mixture model." Communications in Statistics - Theory and Methods 48, no. 23 (December 29, 2018): 5889–910. http://dx.doi.org/10.1080/03610926.2018.1523431.

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28

Zhang, Wan Li, Guo Xin Li, and Wei Gao. "The Research of Speech Emotion Recognition Based on Gaussian Mixture Model." Applied Mechanics and Materials 668-669 (October 2014): 1126–29. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1126.

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A new recognition method based on Gaussian mixture model for speech emotion recognition is proposed in this paper. To improve the effectiveness of feature extraction and accuracy of emotion recognition, extraction of Mel frequency cepstrum coefficient combined with Gaussian mixture model is used to recognize speech emotion. According to feature parameters extraction method by analyzing the principle of vocalization theory, emotion models based on Gaussian mixture model are generated and the similarity of their templates is obtained. A series of experiments is performed with recorded speech based on Gaussian mixture model and indicates the system gains high performance and better robustness.

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29

Han, Da-Jeong, Aa-Ron Park, Jun-Qyu Park, and Sung-June Baek. "Gaussian Mixture Model using Minimum Classification Error for Environmental Sounds Recognition Performance Improvement." Journal of the Korea Contents Association 11, no. 12 (December 28, 2011): 497–503. http://dx.doi.org/10.5392/jkca.2011.11.12.497.

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30

Qiu, Tian. "Model Selection Using Gaussian Mixture Models and Parallel Computing." Journal of Purdue Undergraduate Research 7 (August 31, 2017): 82–83. http://dx.doi.org/10.5703/1288284316412.

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31

Zeng, Jice, and Zhen Hu. "Automated operational modal analysis using variational Gaussian mixture model." Engineering Structures 273 (December 2022): 115139. http://dx.doi.org/10.1016/j.engstruct.2022.115139.

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32

Constantinopoulos, C., M. K. Titsias, and A. Likas. "Bayesian feature and model selection for Gaussian mixture models." IEEE Transactions on Pattern Analysis and Machine Intelligence 28, no. 6 (June 2006): 1013–18. http://dx.doi.org/10.1109/tpami.2006.111.

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33

Zhang, Yi, Miaomiao Li, Siwei Wang, Sisi Dai, Lei Luo, En Zhu, Huiying Xu, Xinzhong Zhu, Chaoyun Yao, and Haoran Zhou. "Gaussian Mixture Model Clustering with Incomplete Data." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 1s (March 31, 2021): 1–14. http://dx.doi.org/10.1145/3408318.

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Gaussian mixture model (GMM) clustering has been extensively studied due to its effectiveness and efficiency. Though demonstrating promising performance in various applications, it cannot effectively address the absent features among data, which is not uncommon in practical applications. In this article, different from existing approaches that first impute the absence and then perform GMM clustering tasks on the imputed data, we propose to integrate the imputation and GMM clustering into a unified learning procedure. Specifically, the missing data is filled by the result of GMM clustering, and the imputed data is then taken for GMM clustering. These two steps alternatively negotiate with each other to achieve optimum. By this way, the imputed data can best serve for GMM clustering. A two-step alternative algorithm with proved convergence is carefully designed to solve the resultant optimization problem. Extensive experiments have been conducted on eight UCI benchmark datasets, and the results have validated the effectiveness of the proposed algorithm.

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34

Ling, Haitian, and Kunping Zhu. "Predicting Precipitation Events Using Gaussian Mixture Model." Journal of Data Analysis and Information Processing 05, no. 04 (2017): 131–39. http://dx.doi.org/10.4236/jdaip.2017.54010.

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35

Kaplan, D. L., J. K. Swiggum, T. D. J. Fichtenbauer, and M. Vallisneri. "A Gaussian Mixture Model for Nulling Pulsars." Astrophysical Journal 855, no. 1 (February 28, 2018): 14. http://dx.doi.org/10.3847/1538-4357/aaab62.

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36

Liu, De Fang, Ming Deng, and Dai Mu Wang. "Background Subtraction Based on Gaussian Mixture Model." Advanced Materials Research 694-697 (May 2013): 2021–26. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2021.

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According to the detection of moving objects in video sequences, the paper puts forward background subtraction based on Gauss mixture model. It analyzes the usual pixel-level approach, and to develop an efficient adaptive algorithm using Gaussian mixture probability density. Recursive equations are used to constantly update the parameters and but also to simultaneously select the appropriate number of components for each pixel.

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37

Lee, Jeongwon, and Choong Ho Lee. "Raining State Study using Gaussian Mixture Model." IJASC 2, no. 3 (September 30, 2020): 21–25. http://dx.doi.org/10.22662/ijasc.2020.2.3.021.

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38

Myers, R., and B. Sotirin. "Gaussian mixture statistical model of Arctic noise." Journal of the Acoustical Society of America 93, no. 4 (April 1993): 2418. http://dx.doi.org/10.1121/1.405925.

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39

Cook, Gregory W. "Gaussian mixture model for edge-enhanced images." Journal of Electronic Imaging 13, no. 4 (October 1, 2004): 731. http://dx.doi.org/10.1117/1.1790507.

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40

Costa, Tommaso, Giuseppe Boccignone, and Mario Ferraro. "Gaussian Mixture Model of Heart Rate Variability." PLoS ONE 7, no. 5 (May 30, 2012): e37731. http://dx.doi.org/10.1371/journal.pone.0037731.

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41

Pinto, Rafael Coimbra, and Paulo Martins Engel. "Correction: A Fast Incremental Gaussian Mixture Model." PLOS ONE 10, no. 10 (October 28, 2015): e0141942. http://dx.doi.org/10.1371/journal.pone.0141942.

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42

Kim, Seungsu, Robert Haschke, and Helge Ritter. "Gaussian Mixture Model for 3-DoF orientations." Robotics and Autonomous Systems 87 (January 2017): 28–37. http://dx.doi.org/10.1016/j.robot.2016.10.002.

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43

Azam, Muhammad, and Nizar Bouguila. "Bounded Generalized Gaussian Mixture Model with ICA." Neural Processing Letters 49, no. 3 (June 25, 2018): 1299–320. http://dx.doi.org/10.1007/s11063-018-9868-7.

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44

Kwon, Hyunjeong, Mingyu Woo, Young Hwan Kim, and Seokhyeong Kang. "Statistical Leakage Analysis Using Gaussian Mixture Model." IEEE Access 6 (2018): 51939–50. http://dx.doi.org/10.1109/access.2018.2870528.

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45

Shervegar, Madhava Vishwanath, and Ganesh V. Bhat. "Heart sound classification using Gaussian mixture model." Porto Biomedical Journal 3, no. 1 (August 2018): e4. http://dx.doi.org/10.1016/j.pbj.0000000000000004.

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46

Aroon, Athira, and S. B. Dhonde. "Speaker Recognition System using Gaussian Mixture Model." International Journal of Computer Applications 130, no. 14 (November 17, 2015): 38–40. http://dx.doi.org/10.5120/ijca2015907193.

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47

Gan, Haitao, Nong Sang, and Rui Huang. "Manifold regularized semi-supervised Gaussian mixture model." Journal of the Optical Society of America A 32, no. 4 (March 12, 2015): 566. http://dx.doi.org/10.1364/josaa.32.000566.

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48

TU, WANGSHU, UTKARSH J. DANG, and SANJEENA SUBEDI. "Change point detection via Gaussian mixture model." Journal of Statistical Research 58, no. 1 (August 14, 2024): 197–219. http://dx.doi.org/10.3329/jsr.v58i1.75425.

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Change point detection aims to find abrupt changes in time series data. These changes denote substantial modifications to the process; these can be modeled as a change in the distribution (in location, scale, or trend). Traditional changepoint detection methods often rely on a cost function to assess if a change occurred in a series. Here, change point detection is investigated in a mixture-model-based clustering framework and a novel change point detection algorithm is developed using a finite mixture of regressions with concomitant variables. Through the introduction of a label correction mechanism, the unstructured clustering-based labels are treated as ordered and distinct segment labels. This approach can detect change points in both univariate and multivariate time series, and different kinds of change can be captured using a parsimonious family of models. Performance is illustrated on both simulated and real data. Journal of Statistical Research 2024, Vol. 58, No. 1, pp. 197-219.

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49

Mishra, Debasish, Krishna R. Pattipati, and George M. Bollas. "Gaussian mixture model for tool condition monitoring." Journal of Manufacturing Processes 131 (December 2024): 1001–13. http://dx.doi.org/10.1016/j.jmapro.2024.09.038.

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50

K Borse, Himani, and Bharati Patil. "Background Subtraction with Dirichlet process Gaussian Mixture Model (DP-GMM) for Motion Detection." International Journal of Scientific Engineering and Research 3, no. 7 (July 27, 2015): 70–75. https://doi.org/10.70729/7071501.

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