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Keywords
(7)
Density Estimation
Em Algorithm
Finite Mixture Model
Gaussian Mixture
Gaussian Mixture Model
Texture Segmentation
Unsupervised Learning
Related Publications
(11)
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Efficient Greedy Learning of Gaussian Mixture Models
Efficient Greedy Learning of Gaussian Mixture Models,10.1162/089976603762553004,Neural Computation,Jakob J. Verbeek,Nikos A. Vlassis,Ben J. A. Kröse
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Efficient Greedy Learning of Gaussian Mixture Models
(
Citations: 117
)
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Jakob J. Verbeek
,
Nikos A. Vlassis
,
Ben J. A. Kröse
This paper concerns the greedy learning of Gaussian mixtures. In the greedy ap proach, mixture components are inserted into the mixture one after the other. We propose a heuristic for searching for the optimal component to insert. In a randomized manner a set of candidate new components is generated. For each of these candidates we find the locally optimal new component. The best local optimum is then inserted into the existing mixture. The resulting algorithm resolves the sensitivity to initializa tion of stateoftheart methods, like EM, and has running time linear in the number of data points and quadratic in the (final) number of mixture components. Due to its greedy nature the algorithm can be particularly useful when the optimal number of mixture components is unknown. Experimental results comparing the proposed algo rithm to other methods on
density estimation
and
texture segmentation
are provided.
Journal:
Neural Computation  NECO
, vol. 15, no. 2, pp. 469485, 2003
DOI:
10.1162/089976603762553004
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Citation Context
(75)
...To reduce the user’s workload, we use the greedy EM algorithm [
35
], which builds models in an adaptive manner...
...Although there is no known constructive method to find the global maximum, the greedy EM algorithm we adopted locates the global maximum using a search heuristic [
35
]...
Yunhai Wang
,
et al.
Efficient Volume Exploration Using the Gaussian Mixture Model
...Moreover, we also compare our approach (we refer to it as EEM) with FigueiredoJain algorithm (FJEM) [11], deterministic annealing based model selection method (DAMS) [17], greedy EM method [
13
], as well as variational component splitting method (VCS) [12]...
Boyu Wang
,
et al.
Entropy penalized learning for Gaussian mixture models
...The traffic phases can be identified using the fundamental diagram flow vs. density, as shown in Fig. 2 for the sensor i =7 . In this example we employ a Gaussian mixture model (GMM) [
8
] clustering method to classify the traffic phase, assuming a mixture of two Gaussian components for the joint distribution of flow and density:...
...where M is the number of components, and gm(·µm,Cm) is the mth Gaussian distribution with (NP +1 )× 1 vector of mean values µm and (NP +1 )× (NP +1 )covariance matrix Cm. Parameters {αm,µ m ,Cm} M=1 are inferred from the historical data provided by the traffic monitoring network using the EM algorithm [7] [
8
]...
...This has an impact on complexity of offline procedures  number of iterations of EM for GMM learning in the order of M 2 D where D is the dimension of dataset [
8
] , and on online steps  computational cost is O(N 2...
A. Pascale
,
et al.
Adaptive Bayesian network for traffic flow prediction
...Greedy learning of GMM, recently proposed in [
32
, 34], overcomes the drawbacks of the EM algorithm (e.g...
...This idea was implemented in [34] and further improved in [
32
], and is utilized in the present work...
Dror Lederman
.
An endotracheal intubation confirmation system based on carina image d...
...method to learn the gaussians mixture model configuration [
9
]...
Nicola Greggio
,
et al.
Unsupervised Greedy Learning of Finite Mixture Models
References
(24)
Learning Mixtures of Gaussians
(
Citations: 168
)
Sanjoy Dasgupta
Conference:
IEEE Symposium on Foundations of Computer Science  FOCS
, pp. 634644, 1999
Maximum likelihood from incomplete data via the em algorithm
(
Citations: 14070
)
Arthur P. Dempster
,
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,
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(
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)
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Published in 1986.
Unsupervised learning of finite mixture models
(
Citations: 661
)
Mário A. T. Figueiredo
,
Anil K. Jain
Journal:
IEEE Transactions on Pattern Analysis and Machine Intelligence  PAMI
, vol. 24, no. 3, pp. 381396, 2002
Vector quantization and signal compression
(
Citations: 3774
)
A. Gersho
,
R. M. Gray
Published in 1992.
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Citations
(117)
Efficient Volume Exploration Using the Gaussian Mixture Model
Yunhai Wang
,
Wei Chen
,
Jian Zhang
,
Tingxing Dong
,
Guihua Shan
,
Xuebin Chi
Journal:
IEEE Transactions on Visualization and Computer Graphics  TVCG
, vol. 17, no. 11, pp. 15601573, 2011
Brain structure segmentation of Magnetic Resonance imaging using tmixture algorithm
C. Anna Palagan
,
T. Leena
Conference:
International Conference on Electronic Computer Technology  ICECT
, 2011
Target tracking algorithm based on improved Gaussian mixture particle filter
Yunbo Kong
,
Xinxi Feng
,
Chuanguo Lu
Conference:
International Conference on Electronic and Mechanical Engineering and Information Technology  EMEIT
, 2011
Entropy penalized learning for Gaussian mixture models
Boyu Wang
,
Feng Wan
,
Peng Un Mak
,
Pui In Mak
,
Mang I Vai
Conference:
International Symposium on Neural Networks  ISNN
, pp. 20672073, 2011
Learning latent variable models from distributed and abstracted data
Xiaofeng Zhang
,
William K. Cheung
,
C. H. Li
Journal:
Information Sciences  ISCI
, vol. 181, no. 14, pp. 29642988, 2011