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A Study of Cluster Validity Indices for Real-Life Data 1

16th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Starczewski, Artur Krzyzak, Adam Czestochowa Tech Univ Inst Computat Intelligence Al Armii Krajowej 36 PL-42200 Czestochowa Poland Concordia Univ Dept Comp Sci & Software Engn Montreal PQ Canada

ISBN: (数字)9783319590608

ISBN: (纸本)9783319590608;9783319590592

In this paper a study of several cluster validity indices for real-life data sets is presented. Moreover, a new version of validity index is also proposed. All these indices can be considered as a measure of data partitioning accuracy and the performance of them is demonstrated for real-life data sets, where three popular algorithms have been applied as underlying clustering techniques, namely the Complete-linkage, expectation maximization and K-means algorithms. The indices have been compared taking into account the number of clusters in a data set. The results are useful to choose the best validity index for a given data set.

关键词： Cluster validity index Complete-linkage clustering K-means clustering expectation maximization algorithm

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Micro-level prediction of outstanding claim counts based on novel mixture models and neural networks

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EUROPEAN ACTUARIAL JOURNAL 2023年第1期13卷 55-90页

作者： Buecher, Axel Rosenstock, Alexander Heinrich Heine Univ Dusseldorf Math Inst Univ Str 1 D-40225 Dusseldorf Germany ARAG SE ARAG Pl 1 D-40472 Dusseldorf Germany

Predicting the number of outstanding claims (IBNR) is a central problem in actuarial loss reserving. Classical approaches like the Chain Ladder method rely on aggregating the available data in form of loss triangles, thereby wasting potentially useful additional claims information. A new approach based on a micro-level model for reporting delays involving neural networks is proposed. It is shown by extensive simulation experiments and an application to a large-scale real data set involving motor legal insurance claims that the new approach provides more accurate predictions in case of non-homogeneous portfolios.

关键词： Loss reserving Individual claim features General insurance Randomly truncated data expectation maximization algorithm Mixture distribution

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Statistical media analysis in video indexing

Statistical media analysis in video indexing

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Internet Multimedia Management Systems III Conference

作者： Naphade, MR IBM Corp Thomas J Watson Res Ctr Pervas Media Management Grp Hawthorne NY 10532 USA

ISBN: (纸本)0819446416

Media analysis for video indexing is witnessing an increasing influence of statistical techniques. Examples of these techniques include the use of generative models as well as discriminant techniques for video structuring, classification, summarization, indexing and retrieval. Advances in multimedia analysis are related directly to advances in signal processing, computer vision, pattern recognition, multimedia databases and smart sensors. This paper highlights the statistical techniques in multimedia retrieval with particular emphasis on semantic characterization.

关键词： Multimedia Analysis Statistical machine learning Decision theory Semantic video indexing probabilistic graphical models expectation maximization algorithm factor graphs

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Speech emotion recognition using Gaussian mixture vector autoregressive models

Speech emotion recognition using Gaussian mixture vector aut...

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32nd IEEE International Conference on Acoustics, Speech and Signal Processing

作者： El Ayadi, Moataz M. H. Kamel, Mohamed S. Karray, Fakhri Univ Waterloo Pattern Anal & Machine Intelligence Lab Waterloo ON N2L 3G1 Canada

ISBN: (纸本)1424407281

It is believed that modeling temporal structure of the speech data may be useful for the problem of speech emotion recognition [1]. In this paper, Gaussian mixture vector autoregressive model is proposed as a statistical classifier for this task. The main motivation behind using such a model is its ability to model the dependency among extracted speech feature vectors as well as the multi-modality in their distribution. When applied to the Berlin emotional speech database, the proposed technique provides a classification accuracy of 76% versus 71% for the hidden Markov model, 67% for the k-nearest neighbors, 55% for feed-forward neural networks. The model gives also better discrimination between high-arousal, low arousal, and neutral emotions than the HMM.

关键词： expectation maximization algorithm Gaussian mixture models maximum likelihood estimation speech emotion recognition vector autoregressive models

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Multi-state Travel Time Reliability Model: Impact of Incidents on Travel Time Reliability

Multi-state Travel Time Reliability Model: Impact of Inciden...

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14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

作者： Park, Sangjun Rakha, Hesham Guo, Feng Virginia Tech Transportat Inst Ctr Sustainable Mobil Dept Civil Engn Blacksburg VA 24061 USA Virginia Tech Dept Stat Blacksburg VA 24061 USA

ISBN: (纸本)9781457721977

This paper attempts to quantify the impact of traffic incidents on travel time reliability using a newly proposed multi-state travel time reliability model. Given that the multi-state travel time reliability model provides significantly better fits when compared to using a single-mode density function, it is possible to quantify the incident impacts more accurately. In order to obtain travel times, the study simulates weekday traffic on a section of I-66 over 17 days, once with incidents and once without them, using the INTEGRATION microscopic traffic simulation software. The simulated travel time data sets are then used to fit a three-state travel time reliability model (three normal distributions) to calibrate the parameters of the density function using the expectation maximization (EM) algorithm. The study demonstrates that incidents do not introduce an additional component distribution when congestion has already onset;instead they increase the mean travel time and variability in travel times for the congested conditions. For instance, the 90th percentile travel time of the second component distribution increases by up to 93 percent. Additionally, the study addresses technical issues related to the calibration and interpretation of the model from a practical standpoint.

关键词： Calibration Data models Delay Density functional theory Reliability Road transportation Vehicles calibration density function expectation maximization algorithm expectation-maximisation algorithm integration microscopic traffic simulation multistate trave

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Robust CSI Estimation under Complex Communication Environment

Robust CSI Estimation under Complex Communication Environmen...

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IEEE International Conference on Communications (IEEE ICC)

作者： Zhang, Haipei Xue, Jiang Meng, Deyu Zhao, Qian Xu, Zongben Shijiazhuang Campus Army Engineering University Shijiazhuang China

ISBN: (纸本)9781538680889

Channel estimation is the critical and fundamental problem in wireless communication techniques, however, the complexity environment, including interference and noise, post a fundamental limit on the accuracy of channel estimation on practical applications. Most existing channel estimation techniques are based on the simple assumption of Gaussian white noise, which makes the performance poorly within real communication environment. To address this problem, we propose a new channel estimation method by assuming the environment as Mixture of Gaussian (MoG) distributions and penalized MoG (PMoG) model by combining the penalized likelihood method with MoG distributions. This model is proposed by the first time in the research of wireless communication, and the superiority of this method lies on its approximation capability to wide range of scenarios of complex communication environments adaptively and analyzing the environment by learning the proper number of statistical components. Moreover, we design an expectation maximization (EM) algorithm to estimate the parameters of the PMoG model. The advantage of our method is demonstrated by simulation experiments.

关键词： Channel estimation Machine learning MIMO Mixture of Gaussian distributions expectation maximization algorithm

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Decision Boundary for Underwater Acoustic Communication with Generalized Gaussian Noise Model 11

Decision Boundary for Underwater Acoustic Communication with...

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11th International Conference on Signal Processing and Communications (SPCOM)

作者： Bhuyan, Snigdha Deshmukh, Siddharth Natl Inst Technol Dept Elect & Commun Engn Rourkela 769008 Odisha India

ISBN: (纸本)9781509017461

In this paper, we consider Generalized Gaussian (GG) distribution to model the additive noise source in underwater acoustic (UWA) communication. Since communication in oceanic medium is dominated by both prevailing and spontaneous noise sources, we model the resultant noise distribution as mixture of GG distribution. Owing to the complexity in optimal detector design with GG noise model, we apply expectation maximization (EM) algorithm to decompose the resultant channel distribution in terms of weighted sum of Gaussian density functions. By having multiple antennas at the receiver, we also exploit spatial diversity to improve error performance at the receiver. In this context, we compute decision boundary for detecting the binary phase shift keying (BPSK) modulated signal. In addition, we also discuss variation in decision boundary under various signal to noise ratio (SNR) levels observed at receiver front end. Finally, we compare the detector performance under new decision boundary with traditional detectors and validate the approach by showing improvement in symbol error rate performance.

关键词： Underwater Acoustic Communication Generalized Gaussian Statistics expectation maximization algorithm Detector Design

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Unsupervised Color Image Segmentation by Clustering into Multivariate Gaussians

Unsupervised Color Image Segmentation by Clustering into Mul...

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5th International Conference on Information Processing (ICIP 2011)

作者： Raj, Jobin Govindan, V. K. Natl Inst Technol Calicut Dept Comp Sci & Engn Calicut Kerala India

ISBN: (纸本)9783642227851

An Unsupervised Color Image Segmentation algorithm by using Finite Gaussian Mixture Model (GMM) is proposed in this paper. The parameters of GMM are estimated using expectation maximization (EM) algorithm. A novel technique for initializing the EM algorithm is presented. The algorithm runs in two stages. The first stage performs an initial segmentation which initializes the EM algorithm. The second stage is the parameter estimation phase using EM algorithm. The scheme is computationally efficient in terms of space and time complexity.

关键词： expectation maximization algorithm Gaussian Mixture Model Image Segmentation Model-based Segmentation Multivariate Gaussian Distribution

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Multiple target tracking using hough transform PMHT algorithm 1

Multiple target tracking using hough transform PMHT algorith...

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1st International IEEE Symposium on Intelligent Systems

作者： Alexiev, KM Bulgarian Acad Sci Cent Lab Parallel Proc BU-1113 Sofia Bulgaria

ISBN: (纸本)0780376013

The problem of estimating the parameters that determine a mixture density has been subject of extraordinary interest in the last years. Such a mixture density estimation problem is multi-target tracking. One of the new data association/tracking algorithms used for multi-target tracking is Probabilistic Multi-Hypothesis Tracker proposed by Streit and Luginbuhl. This paper considers a modification of Probabilistic Multi-Hypothesis Tracker. The implementation of Hough Transform is proposed to generate a good start point to the main PMHT algorithm. The suggested modification improves estimation accuracy and correctness of PMHT algorithm and overcome its main drawback of low convergence.

关键词： multiple target tracking hough transform expectation maximization algorithm

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UNIVERSAL WYNER-ZIV CODING FOR GAUSSIAN SOURCES

UNIVERSAL WYNER-ZIV CODING FOR GAUSSIAN SOURCES

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dupraz, Elsa Roumy, Aline Kieffer, Michel Univ Paris 11 SUPELEC CNRS L2S F-91192 Gif Sur Yvette France INRIA F-35042 Rennes France CNRS Telecom ParisTech LTCI F-75013 Paris France Inst Univ France Paris France

ISBN: (纸本)9781479903566

This paper considers the problem of lossy source coding with side information at the decoder only, for Gaussian sources, when the joint statistics of the sources are partly unknown. We propose a practical universal coding scheme based on scalar quantization and non-binary LDPC codes, which avoids the binarization of the quantized coefficients. We first explain how to choose the rate and to construct the EDPC coding matrix. Then, a decoding algorithm that jointly estimates the source sequence and the joint statistics of the sources is proposed. The proposed coding scheme suffers no loss compared to the practical coding scheme with same rate hut known variance.

关键词： Source coding with side information Rate-distortion Low Density Parity Check codes expectation maximization algorithm

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