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Wavelet/mixture of experts network structure for EEG signals classification

EXPERT SYSTEMS WITH APPLICATIONS 2008年第3期34卷 1954-1962页

作者： Uebeyli, Elif Derya TOBB Ekonomi Teknol Univ Dept Elect & Comp Engn Fac Engn TR-06530 Ankara Turkey

Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide model selection for classification of electroencephalogram (EEG) signals. expectation-maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The EEG signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The ME network structure was implemented for classification of the EEG signals using the statistical features as inputs. To improve classification accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified with the accuracy of 93.17% by the ME network structure. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： mixture of experts expectation-maximization algorithm classification accuracy discrete wavelet transform EEG signals classification

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Detecting variabilities of Doppler ultrasound signals by a modified mixture of experts with diverse features

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DIGITAL SIGNAL PROCESSING 2008年第2期18卷 267-279页

作者： Ubeyli, Elif Derya Guler, Inan TOBB Ekon Teknol Univ Fac Engn Dept Elect & Elect Engn TR-06530 Ankara Turkey Gazi Univ Fac Tech Educ Dept Elect & Comp Educ TR-06500 Ankara Turkey

This paper illustrates the use of modified mixture of experts (MME) network structure to guide model selection for classification of Doppler ultrasound signals with diverse features. The MME is a modular neural network architecture for supervised learning. expectation-maximization (EM) algorithm was used for training the MME so that the learning process is decoupled in a manner that fits well with the modular structure. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Decision making was performed in two stages: feature extraction by spectral analysis methods (fast Fourier transform and model-based methods) and classification using the classifiers trained on the extracted features. In order to discriminate the Doppler ultrasound signals, the ability of designed and trained MME network structure combined with spectral analysis methods was explored. The MME achieved accuracy rates which were higher than that of the mixture of experts (ME) and feedforward neural network models (multilayer perceptron neural network-MLPNN). The proposed MME approach can be useful in classifying the Doppler ultrasound signals for early detection of arterial diseases. (C) 2007 Elsevier Inc. All rights reserved.

关键词： modified mixture of experts expectation-maximization algorithm spectral analysis methods Doppler ultrasound signals diverse features

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An EM approach to multiple-access interference mitigation in asynchronous slow FHSS systems

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2008年第7期7卷 2661-2670页

作者： Tan, Xing Shea, John M. Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

In this paper, we apply the EM algorithm for mitigation of multi-access interference (MAI) in asynchronous slow frequency-hop spread spectrum (FHSS) systems that employ binary frequency-shift keying (BFSK) modulation. MAI occurs if the hopping patterns of the users are not orthogonal. We show that when FSK signals arrive asynchronously, the time offset exposes portions of the desired and interfering signals in a way that can be exploited to improve the decoder performance. We develop an iterative detection, estimation, and decoding scheme to recover the desired signal in the presence of MAI. We compare the performance of this algorithm with that of a conventional noncoherent BFSK transceiver and show that the EM-based algorithm is particularly effective in the presence of strong interfering signals and allows more users in a FHSS system.

关键词： multiple-access interference mitigation frequency hop communication iterative detection and estimation expectation-maximization algorithm multiuser detection

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Multitask Classification by Learning the Task Relevance

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IEEE SIGNAL PROCESSING LETTERS 2008年第1期15卷 593-596页

作者： Fang, Jun Ji, Shihao Xue, Ya Carin, Lawrence Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

We consider the problem of multitask learning (MTL), in which we simultaneously learn classifiers for multiple data sets (tasks), with sharing of intertask data as appropriate. We introduce a set of relevance parameters that control the degree to which data from other tasks are used in estimating the current task's classifier parameters. The set of relevance parameters are learned by maximizing their posterior probability, yielding an expectation-maximization (EM) algorithm. We illustrate the effectiveness of our approach through experimental results on a practical data set.

关键词： Classification expectation-maximization algorithm multitask learning

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Imputation in data fusion of heterogeneous data sets a model-based numerical experiment

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2008年第7期37卷 1316-1328页

作者： Berchtold, Andre Jeannin, Andre Univ Hosp Ctr Grp Rech Sante Adolescents Lausanne Switzerland Univ Lausanne Inst Appl Math Lausanne Switzerland

Given the very large amount of data obtained everyday through population surveys, much of the new research again could use this information instead of collecting new samples. Unfortunately, relevant data are often disseminated into different files obtained through different sampling designs. Data fusion is a set of methods used to combine information from different sources into a single dataset. In this article, we are interested in a specific problem: the fusion of two data files, one of which being quite small. We propose a model-based procedure combining a logistic regression with an expectation-maximization algorithm. Results show that despite the lack of data, this procedure can perform better than standard matching procedures.

关键词： binary variable data fusion data structure expectation-maximization algorithm logistic regression matching

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Probabilistic based recursive model for adaptive processing of data structures

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EXPERT SYSTEMS WITH APPLICATIONS 2008年第2期34卷 1403-1422页

作者： Cho, Siu-Yeung Nanyang Technol Univ Sch Comp Engn Div Comp Syst Singapore 639798 Singapore

One of the most popular frameworks for the adaptive processing of data structures to date, was proposed by Frasconi et al. [Frasconi, P., Gori, M., & Sperduti, A. (1998). A general framework for adaptive processing of data structures. IEEE Transactions oil Neural Networks, 9(September), 768-785], who used a Backpropagation Through Structures (BPTS) algorithm [Goller, C., & Kuchler, A. (1996). Learning task-dependent distributed representations by back-propagation through structures. In Proceedings of IEEE international conference on neural networks (pp. 347-352);Tsoi, A. C. (1998). Adaptive processing of data structure: An expository overview and comments. Technical report in Faculty Informatics. Wollongong, Australia: University of Wollongong] to carry out supervised learning. This supervised model has been successfully applied to a number of learning tasks that involve complex symbolic structural patterns, such as image semantic structures, internet behavior, and chemical compounds. In this paper, we extend this model, using probabilistic estimates to acquire discriminative information from the learning patterns. Using this probabilistic estimation, smooth discriminant boundaries can be obtained through a process of clustering onto the observed input attributes. This approach enhances the ability of class discrimination techniques to recognize structural patterns. The proposed model is represented by a set of Gaussian Mixture Models (GMMs) at the hidden layer and a set of "weighted sum input to sigmoid function" models at the output layer. The proposed model's learning framework is divided into two phases: (a) locally unsupervised learning for estimating the parameters of the GMMs and (b) globally supervised learning for fine-tuning the GMMs' parameters and optimizing weights at the output layer. The unsupervised learning phase is formulated as a maximum likelihood problem that is solved by the expectation-maximization (EM) algorithm. The supervised learning phase

关键词： adaptive processing of data structures probabilistic recursive model Gaussian mixture model expectation-maximization algorithm Levenberg-Marquardt algorithm

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Estimation of space-time branching process models in seismology using an EM-type algorithm

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2008年第482期103卷 614-624页

作者： Veen, Alejandro Schoenberg, Frederic P. IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA Univ Calif Los Angeles Dept Stat Los Angeles CA 90095 USA

Maximum likelihood estimation of branching point process models via numerical optimization procedures can be unstable and computationally intensive. We explore an alternative estimation method based on the expectation-maximization algorithm. The method involves viewing the estimation of such branching processes as analogous to incomplete data problems. Using an application from seismology, we show how the epidemic-type aftershock sequence (ETAS) model can, in fact, be estimated this way, and we propose a computationally efficient procedure to maximize the expected complete data log-likelihood function. Using a space-time ETAS model, we demonstrate that this method is extremely robust and accurate and use it to estimate declustered background seismicity rates of geologically distinct regions in Southern California. All regions show similar declustered background intensity estimates except for the one covering the southern section of the San Andreas fault system to the east of San Diego in which a substantially higher intensity is observed.

关键词： branching process models earthquakes epidemic-type aftershock sequence model expectation-maximization algorithm maximum likelihood space-time point process models

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Information loss for 2 x 2 tables with missing cell counts: binomial case

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STATISTICA NEERLANDICA 2008年第2期62卷 239-254页

作者： Eisinga, Rob Radboud Univ Nijmegen Dept Social Sci Res Methods NL-6500 HE Nijmegen Netherlands

We formulate likelihood-based ecological inference for 2 x 2 tables with missing cell counts as an incomplete data problem and study Fisher information loss by comparing estimation from complete and incomplete data. In so doing, we consider maximum-likelihood (ML) estimators of probabilities governed by two independent binomial distributions and obtain simplified expressions for their covariance. These expressions reflect well the additional uncertainty arising from the unobserved data compared to complete data tables. We also discuss an approximation to the expected conditional variance of the unobserved entries and ML parameter bias correction. An empirical example is used to demonstrate the results.

关键词： ecological inference Fisher information loss expectation-maximization algorithm missing information principle parameter bias correction 2 x 2 table

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Homography-based Image Mosaicing by Using EM algorithm for Removing Inconsistent Overlaid Regions

Homography-based Image Mosaicing by Using EM Algorithm for R...

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Annual Conference of the SICE

作者： Iyoshi, Takeaki Mitsuhashi, Wataru Univ Electrocommun Dept Informat & Commun Engn Tokyo Japan

ISBN: (纸本)9784907764302

Image mosaicing is an effective means of constructing a single panoramic image from a series of snapshots taken in different viewing angles. However, in the case of congested traffic scenes with a cluttered environment including vehicles or pedestrians, there are severe difficulties in aligning a pair of snapshots. In such cases, some objects would be taken only in one of the image pair, thereby resulting in failure in stitching the pair of images. This paper deals with three types of techniques for performing an image mosaicing: Homography estimation for determining geometrical relationships between the image pair, expectation-maximization algorithm for removing inconsistent overlapping region, and Dijkstra's algorithm to find the boundary for stitching the images together. Experimental results indicate that the proposed technique is effective to synthesize a panoramic image from a series of narrow-field-of-view snapshots.

关键词： Homography Image Mosaicing expectation-maximization algorithm Dijkstra's algorithm

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ML and EM algorithm for non-data-aided SNR estimation of linearly modulated signals

ML and EM algorithm for non-data-aided SNR estimation of lin...

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6th International Symposium on Communication Systems, Networks and Digital Signal Processing

作者： Gappmair, W. Lopez-Valcarce, R. Mosquera, C. Graz Univ Technol Inst Commun Networks & Satellite Commun A-8010 Graz Austria Univ Vigo Sig Theory & Commun Dept Vigo Spain

ISBN: (纸本)9781424418756

The recently published Cramer-Rao lower bound for non-data-aided (NDA) estimation of the signal-to-noise ratio (SNR) reveals a considerable gap, when compared to the jitter performance of NDA algorithms available from the open literature. The maximum-likelihood (ML) solution derived in this paper closes this gap. However, the latter provides a set of two nonlinear vector equations, which might be simplified only for modulation schemes with constant envelope like M-ary PSK. For signals with nonconstant envelope, like 16-QAM as most prominent example in this respect, a much less complex approach based on the expectation-maximization (EM) principle is developed in this paper. In the medium SNR range, this bridges part of the performance gap mentioned previously. Over the full SNR range, we propose a hybrid algorithm, where the EM estimate is replaced by a moment-based method as soon as the true SNR drops below a predefined threshold.

关键词： digital modulation SNR estimation maximum-likelihood solution expectation-maximization algorithm

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