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An Efficient EM-algorithm for Big data in Wireless Sensor Network using Mobile Sink
unpublished
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of the big data. ...
Although the data generated by an individual sensor may not appear to be significant, the overall data generated across numerous sensors in the densely distributed WSNs can produce a significant portion ...
However, the EM algorithm minimizes the sum of square of direct distance, not communication distance. ...
fatcat:iuf7w3vqmfe5rmh37gky6iexxq
Using data to build a better EM: EM* for big data
2017
International Journal of Data Science and Analytics
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, unsupervised algorithms like expectation maximization algorithm for clustering (EM-T). ...
Existing data mining techniques, more particularly iterative learning algorithms, become overwhelmed with big data. ...
Acknowledgements The authors thank the editor and anonymous reviewers for their informative comments and suggestions. This work was partially supported by NCI Grant 1R01CA213466-01. ...
doi:10.1007/s41060-017-0062-1
dblp:journals/ijdsa/KurbanJD17
fatcat:flvfllrcqjhmpmkjc5eo3h663e
EM-MAC
2011
Proceedings of the Twelfth ACM International Symposium on Mobile Ad Hoc Networking and Computing - MobiHoc '11
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This paper presents the design and evaluation of the EM-MAC (Efficient Multichannel MAC) protocol, which addresses these challenges through the introduction of novel mechanisms for adaptive receiver-initiated ...
Medium access control (MAC) protocols for wireless sensor networks face many challenges, including energy-efficient operation and robust support for varying traffic loads, in spite of effects such as wireless ...
Acknowledgments We thank our shepherd, Craig Partridge, for guiding us through the paper revisions. We also thank the anonymous reviewers for their valuable suggestions on improving the paper. ...
doi:10.1145/2107502.2107533
dblp:conf/mobihoc/TangSGJ11
fatcat:t4pfdo6l7fbdtde5jxkhn3nzta
Desfechos em estudos de avaliação econômica em saúde
2016
Epidemiologia e Serviços de Saúde
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Most recently, researchers have decided to use big databases (big data) and electronic medical records as information sources. ...
Information sources on the outcomes Reliable clinical guidelines establish the diagnosis criteria, the treatment algorithm and the mechanisms for clinical monitoring. ...
doi:10.5123/s1679-49742016000300023
pmid:27869938
fatcat:vlbwe62pqnfrxe6k7dlsnjyt4u
GAN-EM: GAN based EM learning framework
[article]
2018
arXiv
pre-print
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Expectation maximization (EM) algorithm is to find maximum likelihood solution for models having latent variables. ...
Since our model is unsupervised, the class label of real data is regarded as latent variable, which is estimated by an additional network (E-net) in E-step. ...
The pseudo code is in Algorithm 1. Algorithm 1 GAN-EM 1: Initialization: w i = 1/K f or i = 1 . . . K 2: for iteration = 1 . . . n do 3: update φ: M-step 4: φ i = N i /N f or i = 1 . . . ...
arXiv:1812.00335v1
fatcat:zuiur6wq4vh7tmy7xuinkdh2ni
EM-Cube
1997
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS workshop on Graphics hardware - HWWS '97
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EM-Cube is a VLSI architecture for low-cost, high quality volume rendering at full video frame rates. ...
The performance target for this configuration is to render images from a 256 3 16 bit data set at 30 frames/sec. ...
We process each section in turn using the EM-Cube algorithm and then combine the results. ...
doi:10.1145/258694.258731
fatcat:wv6k4n7gefhoncytpgfvosjc6m
On the EM-Tau algorithm: a new EM-style algorithm with partial E-steps
[article]
2017
arXiv
pre-print
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In this paper, we introduce a new EM-style algorithm that implements a novel policy for performing partial E-steps. ...
While often used for imputing missing data, its widespread applications include other common statistical tasks, such as clustering. ...
There, the data space for the Estep is reduced monotonically at each iteration, leading to an approximation of the traditional EM result. ...
arXiv:1711.07814v1
fatcat:fyrxy5vllnf5rpv3adrgo2clh4
Stochastic Discriminative EM
[article]
2014
arXiv
pre-print
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Stochastic discriminative EM (sdEM) is an online-EM-type algorithm for discriminative training of probabilistic generative models belonging to the exponential family. ...
In this work, we introduce and justify this algorithm as a stochastic natural gradient descent method, i.e. a method which accounts for the information geometry in the parameter space of the statistical ...
Acknowledgments This work has been partially funded from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no 619209 (AMIDST ...
arXiv:1410.1784v1
fatcat:pow7y7ghnrbg5eohhwpnw5vscy
Fast Online EM for Big Topic Modeling
2016
IEEE Transactions on Knowledge and Data Engineering
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2013 pre-print arXiv:1210.2179v2 fatcat:mkb5vjtmgfffxbzir4s6tcg2aiOther Versions
However, batch EM has high time and space complexities to learn big LDA models from big data streams. ...
to handle both big data and big models (aka, big topic modeling) on just a PC. ...
However, the batch LDA algorithm has a high time complexity for big data sets. ...
doi:10.1109/tkde.2015.2492565
fatcat:ianwwo4j3jhevnvs54skxmt7ma
Edge Detection using Stationary Wavelet Transform, HMM, and EM algorithm
[article]
2020
arXiv
pre-print
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Stationary Wavelet Transform (SWT) is an efficient tool for edge analysis. ...
Laplacian and Gaussian model is used to check the information of the state is an edge or no edge. This model is trained by an EM algorithm and the Viterbi algorithm is employed to recover the state. ...
EXPECTATION-MAXIMIZATION ALGORITHM: We adopt an EM algorithm [7] to train both the model. ...
arXiv:2004.11296v1
fatcat:vtt34o3kjjbpvjrjo4jpjwlhle
EM vs MM: A case study
2012
Computational Statistics & Data Analysis
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For many problems, EM and MM derivations yield the same algorithm. ...
An EM-MM hybrid algorithm is derived which shows faster convergence than the MM algorithm in certain parameter regimes. ...
We begin with the EM algorithm for maximizing (4).
EM algorithm Derivation of EM algorithm hinges upon a missing data structure. ...
doi:10.1016/j.csda.2012.05.018
pmid:23997380
pmcid:PMC3755471
fatcat:cfra76lxnbaljbruutb4dkpdc4
Fair Marriage Principle and Initialization Map for the EM Algorithm
[article]
2020
arXiv
pre-print
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The popular convergence theory of the EM algorithm explains that the observed incomplete data log-likelihood L and the complete data log-likelihood Q are positively correlated, and we can maximize L by ...
The Deterministic Annealing EM (DAEM) algorithm was hence proposed for avoiding locally maximal Q. ...
This map must be useful for the initialization of the EM or an improved EM algorithm for binary Gaussian mixture models and be helpful for other mixture models. ...
arXiv:2007.12845v1
fatcat:zlyfdeytkzh3doqdtyeqippz6u
An Ensemble EM Algorithm for Bayesian Variable Selection
2021
Bayesian Analysis
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Due to its particular updating scheme, our algorithm can be implemented efficiently without inverting a large matrix in each iteration and therefore can scale up with big data. ...
To address this issue, we propose an ensemble EM algorithm, in which we repeatedly apply our EM algorithm to a subset of the samples with a subset of the covariates, and then aggregate the variable selection ...
We proposed an EM algorithm that returns the MAP estimator of the set of relevant variables. The algorithm can be operated very efficiently and therefore can scale up with big data. ...
doi:10.1214/21-ba1275
fatcat:zbc4j5k7xbe4bah6ueieesdewe
From the EM Algorithm to the CM-EM Algorithm for Global Convergence of Mixture Models
[article]
2018
arXiv
pre-print
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For the same example, the EM, MM, and CM-EM algorithms need about 36, 18, and 9 iterations respectively. ...
The Expectation-Maximization (EM) algorithm for mixture models often results in slow or invalid convergence. ...
So, the author proposed the CM-EM algorithm, an improved EM or MM algorithm. ...
arXiv:1810.11227v1
fatcat:o6vsrx56fnb6zctmgay64ibiky
Online EM for Functional Data
[article]
2016
arXiv
pre-print
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A novel approach to perform unsupervised sequential learning for functional data is proposed. ...
Our sequential inference framework is significantly more computationally efficient than equivalent batch learning algorithms, especially when the missing data is high-dimensional. ...
In this perspective, MCoEM can be regarded as a linearization of stochastic batch EM algorithms, which can be particularly appealing in a Big Data context. ...
arXiv:1604.00570v1
fatcat:44kzhojuwralnodhvivhxzdoju
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