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IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

作者： Zamiri-Jafarian, H. Bokharaiee-Najafee, S. Pasupathy, S. Ferdowsi Univ Mashhad Dept Elect Engn Mashhad Iran Univ Toronto Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9781424411894

A new EM based semi-blind beamforming algorithm is proposed in this paper for multiple input multiple output (MIMO) systems in which orthogonal frequency division multiplexing (OFDM) technique is used. Employing singular value decomposition (SVD) technique, the semi-blind beamforming algorithm eliminates space interference in an iterative manner by shaping antenna patterns in transmitter and receiver sides in frequency domain. A 16-QAM MIMO-OFDM system is considered for computer simulations in order to evaluate the performance of the new algorithm under different scenarios. The results show that the proposed algorithm outperforms subspace based semi-blind beamforming method.

关键词： beamforming expectation maximization algorithm MIMO OFDM semi-blind SVD

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Application of Hidden Markov Models in Stock Trading 6

Application of Hidden Markov Models in Stock Trading

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6th International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Chandrika, P., V Visalakshmi, K. Srinivasan, K. Sakthi Vellore Inst Technol VITBS Vellore Tamil Nadu India

ISBN: (纸本)9781728151977;9781728151960

The stock market index forecasting is quite a popular topic in the present economy. There are many micro and macro-economic factors which influence the stock prices. With the emergence of machine learning techniques, algorithmic trading became most popular in forecasting the stock prices. There are many traditional machine learning techniques like ARMA, ARIMA which are used to forecast the stock market index. However these techniques consider trends and patterns involved in the data. Since the stock market data is a time series and irregular in nature there will be some noise involved. The present research paper studies the forecasting of stock market index by applying Hidden Markov models which considers the hidden states within the stock market index and traditional ARIMA model. 11MM considers the posterior probabilities on different hidden states using expectation maximization algorithm. The data considered for the study includes 5 different stock market index Dow Jones, NIFTY 50, 5 & P 500, New York Stock Index (NYSE) and KOSPL The data includes daily prices of Low, High, Close, Open, Volume for a period of 5 years that is 2014-2019 which accounts to approximately 1380 data points. In 11MM model the closing price of the index is considered for determining the transition states and posterior probabilities at 2, 3, 4 and 5 hidden states. Akaike Information Criteria (AIC) and Bayesian information Criterion (BIC) are used to determine the states from HMM. The research paper studies the direction of the market the closing price of the stock index considering HMM. The paper is divided into 5 parts which include Part 1: Introduction, Part 2: Past study, Part 3: Machine Learning algorithms, Part4: Data Analysis using HMM and Part 5: Results and Conclusion.

关键词： Stock Market Index Hidden Markov Modth Posterior probabilities expectation maximization algorithm Akaike Information Criterion Bayesian Information Criterion

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POINT-SPREAD FUNCTION RETRIEVAL FOR FLUORESCENCE MICROSCOPY

POINT-SPREAD FUNCTION RETRIEVAL FOR FLUORESCENCE MICROSCOPY

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IEEE International Symposium on Biomedical Imaging - From Nano to Macro

作者： Pankajakshan, Praveen Blanc-Feraud, Laure Kam, Zvi Zerubia, Josiane INRIA I3S ARIANA Project Team 2004 Route LuciolesBP 93 F-06902 Sophia Antipolis France Weizmann Inst Sci Dept Mol Cell Biol IL-76100 Rehovot Israel

ISBN: (纸本)9781424439317

In this paper we propose a method for retrieving the Point Spread Function (PSF) of an imaging system given the observed image sections of a fluorescent microsphere. Theoretically calculated PSFs often lack the experimental or microscope specific signatures while empirically obtained data are either over sized or (and) too noisy. The effect of noise and the influence of the microsphere size can be mitigated from the experimental data by using a Maximum Likelihood expectation maximization (MLEM) algorithm. The true experimental parameters can then be estimated by fitting the result to a model based on the scalar diffraction theory with lower order Spherical Aberration (SA). The algorithm was tested on some simulated data and the results obtained validate the usefulness of the approach for retrieving the PSF from measured data.

关键词： fluorescence microscopy point-spread function expectation maximization algorithm deconvolution spherical aberration

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Information-theoretic Analysis of the Gibbs algorithm: An Individual Sample Approach

Information-theoretic Analysis of the Gibbs Algorithm: An In...

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2024 Information Theory Workshop

作者： Zhu, Youheng Bu, Yuheng Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China Univ Florida ECE Dept Gainesville FL USA

ISBN: (纸本)9798350348941;9798350348934

Recent progress has shown that the generalization error of the Gibbs algorithm can be exactly characterized using the symmetrized KL information between the learned hypothesis and the entire training dataset. However, evaluating such a characterization is cumbersome, as it involves a high-dimensional information measure. In this paper, we address this issue by considering individual sample information measures within the Gibbs algorithm. Our main contribution lies in establishing the asymptotic equivalence between the sum of symmetrized KL information between the output hypothesis and individual samples and that between the hypothesis and the entire dataset. We prove this by providing explicit expressions for the gap between these measures in the non-asymptotic regime. Additionally, we characterize the asymptotic behavior of various information measures in the context of the Gibbs algorithm, leading to tighter generalization error bounds. An illustrative example is provided to verify our theoretical results, demonstrating our analysis holds in broader settings.

关键词： expectation maximization algorithm

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On the boundary problems in diagnostic classification models

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Behaviormetrika 2023年第1期50卷 399-429页

作者： Yamaguchi, Kazuhiro Faculty of Human Science Institutes of Human Sciences A314 University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba 305-0006 Japan

In diagnostic classification models, parameter estimation sometimes provides estimates that stick to the boundaries of the parameter space, which is called the boundary problem and may lead to extreme values of standard errors. However, the relationship between the boundary problem and irregular standard errors has not been analytically explored. In addition, prior research has not shown how maximum-a-posteriori estimates avoid the boundary problem and affect the standard errors of estimates. To analyze these relationships, the expectation–maximization algorithm for maximum-a-posteriori estimates and a complete data Fisher information matrix are explicitly derived for a mixture formulation of saturated diagnostic classification models. Theoretical considerations show that the emptiness of attribute mastery patterns causes both the boundary problem and the inaccurate standard error estimates. Furthermore, unfortunate boundary problem without emptiness causes shorter standard errors. A simulation study shows that the maximum-a-posteriori method prevents boundary problems. Moreover, this method with monotonicity constraint estimation improves standard error estimates more than unconstrained maximum likelihood estimates do. © 2022, The Behaviormetric Society.

关键词： Diagnostic classification models expectation maximization algorithm Maximum a posteriori estimation Standard error

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Factor extraction using Kalman filter and smoothing: This is not just another survey

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INTERNATIONAL JOURNAL OF FORECASTING 2021年第4期37卷 1399-1425页

作者： Poncela, Pilar Ruiz, Esther Miranda, Karen Univ Autonoma Madrid Dept Econ Anal Quantitat Econ Madrid Spain Univ Carlos III Madrid Dept Stat Madrid Spain

Dynamic factor models have been the main "big data'' tool used by empirical macroeconomists during the last 30 years. In this context, Kalman filter and smoothing (KFS) procedures can cope with missing data, mixed frequency data, time-varying parameters, non-linearities, non-stationarity, and many other characteristics often observed in real systems of economic variables. The main contribution of this paper is to provide a comprehensive updated summary of the literature on latent common factors extracted using KFS procedures in the context of dynamic factor models, pointing out their potential limitations. Signal extraction and parameter estimation issues are separately analyzed. Identification issues are also tackled in both stationary and non-stationary models. Finally, empirical applications are surveyed in both cases. This survey is relevant to researchers and practitioners interested not only in the theory of KFS procedures for factor extraction in dynamic factor models but also in their empirical application in macroeconomics and finance. (C) 2021 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

关键词： Dynamic factor model expectation maximization algorithm Identification Macroeconomic forecasting State-space model

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Finding the Key Influences on the House Price by Finite Mixture Model Based on the Real Estate Data in Changchun 24th

Finding the Key Influences on the House Price by Finite Mixt...

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24th Int Conference on Database Systems for Advanced Applications / 6th Int Workshop on Big Data Management and Service / 4th Int Workshop on Big Data Quality Management / 3rd Int Workshop on Graph Data Management and Analysis

作者： Xu, Xin Huang, Zeyu Wu, Jingyi Fu, Yanjie Luo, Na Chen, Weitong Wang, Jianan Yin, Minghao Northeast Normal Univ Changchun Jilin Peoples R China Missouri Univ Sci & Technol Rolla MO 65409 USA Univ Queensland Brisbane Qld Australia

ISBN: (纸本)9783030185909;9783030185893

Nowadays it's difficult for us to analyze the development law of real estate. What's more, predictable house price and understandable key influences can also build a healthier real estate market. Therefore, we propose a model which can predict the house price, while it can find key influences which are important influences on the house price. Our method is inspired by the finite mixture model (FMM) and information gain ratio (IGO). Specifically, we collect data that includes detail information about houses and communities from Anjuke Inc. which is an online platform for house sales. Then, according to the data, we find the scope of latent groups number by cluster methods to avoid blind searching the number of latent groups. Next, we use IGO to rank the features and weight them and we build a regression model based on the finite mixture model. Finally, the experimental results demonstrate our method performance on predicting house price, and we find key influences on house price.

关键词： Real estate Finite mixture model Information gain ratio expectation maximization algorithm

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ADAPTATIVE CONTEXT NORMALIZATION: A BOOST FOR DEEP LEARNING IN IMAGE PROCESSING 31

ADAPTATIVE CONTEXT NORMALIZATION: A BOOST FOR DEEP LEARNING ...

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2024 International Conference on Image Processing

作者： Faye, Bilal Azzag, Hanane Lebbah, Mustapha Bouchaffra, Djamel Sorbonne Paris Nord Univ LIPN CNRS UMR 7030 Villetaneuse France Univ Paris Saclay David Lab UVSQ F-78035 Versailles France Ctr Dev Adv Technol Algiers Algeria

ISBN: (纸本)9798350349405;9798350349399

Deep Neural network learning for image processing faces major challenges related to changes in distribution across layers, which disrupt model convergence and performance. Activation normalization methods, such as Batch Normalization (BN), have revolutionized this field, but they rely on the simplified assumption that data distribution can be modelled by a single Gaussian distribution. To overcome these limitations, Mixture Normalization (MN) introduced an approach based on a Gaussian Mixture Model (GMM), assuming multiple components to model the data. However, this method entails substantial computational requirements associated with the use of expectation-maximization algorithm to estimate parameters of each Gaussian components. To address this issue, we introduce Adaptative Context Normalization (ACN), a novel supervised approach that introduces the concept of "context", which groups together a set of data with similar characteristics. Data belonging to the same context are normalized using the same parameters, enabling local representation based on contexts. For each context, the normalized parameters, as the model weights are learned during the backpropagation phase. ACN not only ensures speed, convergence, and superior performance compared to BN and MN but also presents a fresh perspective that underscores its particular efficacy in the field of image processing. We release our code at https://***/b-faye/Adaptative-Context-Normalization.

关键词： expectation maximization algorithm

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Reachability Analysis of Human-in-the-Loop Systems Using Gaussian Mixture Model with Side Information

Reachability Analysis of Human-in-the-Loop Systems Using Gau...

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IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

作者： Yang, Cheng-Han Choi, Joonwon Anandavell, Suriyan Hwang, Inseok Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47907 USA

ISBN: (纸本)9798350355376;9798350355369

In the context of a Human-in-the-Loop (HITL) system, the accuracy of reachability analysis plays a significant role in ensuring the safety and reliability of HITL systems. In addition, one can avoid unnecessary conservativeness by explicitly considering human control behavior compared to those methods that rely on the system dynamics alone. One possible approach is to use a Gaussian Mixture Model (GMM) to encode human control behavior using the expectation-maximization (EM) algorithm. However, relatively few works consider the admissible control input ranges due to physical or mechanical limitations when modeling human control behavior. This could make the following reachability analysis overestimate the system's capability, thereby affecting the performance of the HITL system. To address this issue, we present a constrained stochastic reachability analysis algorithm that can explicitly account for the admissible control input ranges. By confining the ellipsoidal confidence region of each Gaussian component using Sequential Quadratic Programming (SQP), we probabilistically constrain the GMM as well as the corresponding stochastic reachable sets. A comprehensive mathematical analysis of how the constrained GMM can affect the stochastic reachable sets is provided in this paper. Finally, the proposed stochastic reachability analysis algorithm is validated via an illustrative numerical example.

关键词： expectation maximization algorithm

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Joint Iterative Channel Estimation and Data Detection for MIMO-CDMA Systems over Frequency-Selective Fading Channels

Joint Iterative Channel Estimation and Data Detection for MI...

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IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Assra, Ayman Hamouda, Walaa Youssef, Amr Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9781424456383

In this paper, we present an iterative joint channel estimation and data detection technique for multiple-input multiple-output (MIMO) code-division multiple-access (CDMA) systems over frequency-selective fading channels. Based on the expectation-maximization (EM) algorithm, the proposed iterative receiver achieves a performance close to the optimum maximum-likelihood (ML) receiver. In addition, the performance of the proposed receiver is optimized through weight coefficients using the minimum mean-square error (MMSE) criterion. Compared to the single-user bound, our results show that the proposed receiver can mitigate the multiple-access interference and attain the full system diversity. Furthermore, our simulation results confirm that the proposed receiver is near-far resistant and offers fast convergence in severe near-far scenarios.

关键词： MIMO communication MIMO-CDMA system channel estimation code division multiple access data detection technique diversity reception expectation maximization algorithm expectation-maximisation algorithm fading channels frequency selective fading channel full

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