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Multi-kernel Gaussian process latent variable regression model for high-dimensional sequential data modeling

NEUROCOMPUTING 2019年 348卷 3-15页

作者： Zhu, Ziqi Zhang, Jiayuan Zou, Jixin Deng, Chunhua Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China Hubei Key Lab Intelligent Informat Proc & Real Ti Wuhan Hubei Peoples R China Minist Publ Secur Inst Forens Sci Beijing Peoples R China

modeling sequential data has been a hot research field for decades. One of the most challenge problems in this field is modeling real-world high-dimensional sequential data with limited training samples. This is mainly due to the following two reasons. First, if the dimension of the data is significantly greater then the number of the data, it may result in the over-fitting problem. Second, the dynamic behavior of the real-world data is very complex and difficult to approximate. To overcome these two problems, we propose a multi-kernel Gaussian process latent variable regression model for high-dimensional sequential data modeling and prediction. In our model, we design a regression model based on the Gaussian process latent variable model. Furthermore, a multi-kernel learning model is designed to automatically construct suitable nonlinear kernel for various types of sequential data. We evaluate the effectiveness of our method using two types of real-world high-dimensional sequential data, including the human motion data and the motion texture video data. In addition, our method is compared with several representative sequential data modeling methods. Experimental results show that our method achieves promising modeling capability and is capable of predict human motion and texture video with higher quality. (C) 2018 Elsevier B.V. All rights reserved.

关键词： sequential data modeling High-dimensional data Kernel learning Gaussian process latent variable model

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Robust sequential data modeling Using an Outlier Tolerant Hidden Markov Model

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2009年第9期31卷 1657-1669页

作者： Chatzis, Sotirios P. Kosmopoulos, Dimitrios I. Varvarigou, Theodora A. Univ Miami Ctr Computat Sci Coral Gables FL 33146 USA NCSR Demokritos Athens 15310 Greece Natl Tech Univ Athens Dept Elect & Comp Engn GR-15773 Athens Greece

Hidden Markov (chain) models using finite Gaussian mixture models as their hidden state distributions have been successfully applied in sequential data modeling and classification applications. Nevertheless, Gaussian mixture models are well known to be highly intolerant to the presence of untypical data within the fitting data sets used for their estimation. Finite Student's t-mixture models have recently emerged as a heavier-tailed, robust alternative to Gaussian mixture models, overcoming these hurdles. To exploit these merits of Student's t-mixture models in the context of a sequential data modeling setting, we introduce, in this paper, a novel hidden Markov model where the hidden state distributions are considered to be finite mixtures of multivariate Student's t-densities. We derive an algorithm for the model parameters estimation under a maximum likelihood framework, assuming full, diagonal, and factor-analyzed covariance matrices. The advantages of the proposed model over conventional approaches are experimentally demonstrated through a series of sequential data modeling applications.

关键词： Hidden Markov models student's t-distribution expectation-maximization factor analysis sequential data modeling

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LSTM-SDM: An integrated framework of LSTM implementation for sequential data modeling

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SOFTWARE IMPACTS 2022年 14卷

作者： Bhandari, Hum Nath Rimal, Binod Pokhrel, Nawa Raj Rimal, Ramchandra Dahal, Keshab R. Roger Williams Univ Dept Math Bristol RI USA Florida Atlantic Univ Dept Math Sci Boca Raton FL 33431 USA Xavier Univ Louisiana Dept Phys & Comp Sci New Orleans LA USA Middle Tennessee State Univ Dept Math Sci Murfreesboro TN USA Truman State Univ Dept Stat Kirksville MO USA

LSTM-SDM is a python-based integrated computational framework built on the top of Tensorflow/Keras and written in the Jupyter notebook. It provides several object-oriented functionalities for implementing single layer and multilayer LSTM models for sequential data modeling and time series forecasting. Multiple subroutines are blended to create a conducive user-friendly environment that facilitates data exploration and visualization, normalization and input preparation, hyperparameter tuning, performance evaluations, visualization of results, and statistical analysis. We utilized the LSTM-SDM framework in predicting the stock market index and observed impressive results. The framework can be generalized to solve several other real-world time series problems.

关键词： LSTM Prediction Deep learning sequential data modeling Time series data science

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Margin-maximizing classification of sequential data with infinitely-long temporal dependencies

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第11期40卷 4519-4527页

作者： Chatzis, Sotirios P. Cyprus Univ Technol Dept Elect Engn Nicosia Cyprus

Generative models for sequential data are usually based on the assumption of temporal dependencies described-by a first-order Markov chain. To ameliorate this shallow modeling assumption, several authors have proposed models with higher-order dependencies. However, the practical applicability of these approaches is hindered by their prohibitive computational costs in most cases. In addition, most existing approaches give rise to model training algorithms with objective functions that entail multiple spurious local optima, thus requiring application of tedious countermeasures to avoid getting trapped to bad model estimates. In this paper, we devise a novel margin-maximizing model with convex objective function that allows for capturing infinitely-long temporal dependencies in sequential datasets. This is effected by utilizing a recently proposed nonparametric Bayesian model of label sequences with infinitely-long temporal dependencies, namely the sequence memoizer, and training our model using margin maximization and a versatile mean-field-like approximation to allow for increased computational efficiency. As we experimentally demonstrate, the devised margin-maximizing construction of our model, which leads to a convex optimization scheme, without any spurious local optima, combined with the capacity of our model to capture long and complex temporal dependencies, allow for obtaining exceptional pattern recognition performance in several applications. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： sequential data modeling Sequence memoizer Mean-field principle Margin maximization

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Echo State Gaussian Process

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2011年第9期22卷 1435-1445页

作者： Chatzis, Sotirios P. Demiris, Yiannis Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn London SW7 2BT England

Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN) training, with an RNN (the reservoir) being generated randomly, and only a readout being trained using a simple computationally efficient algorithm. ESNs have greatly facilitated the practical application of RNNs, outperforming classical approaches on a number of benchmark tasks. In this paper, we introduce a novel Bayesian approach toward ESNs, the echo state Gaussian process (ESGP). The ESGP combines the merits of ESNs and Gaussian processes to provide a more robust alternative to conventional reservoir computing networks while also offering a measure of confidence on the generated predictions (in the form of a predictive distribution). We exhibit the merits of our approach in a number of applications, considering both benchmark datasets and real-world applications, where we show that our method offers a significant enhancement in the dynamical data modeling capabilities of ESNs. Additionally, we also show that our method is orders of magnitude more computationally efficient compared to existing Gaussian process-based methods for dynamical data modeling, without compromises in the obtained predictive performance.

关键词： Bayesian inference Gaussian process reservoir computing sequential data modeling

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The copula echo state network

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PATTERN RECOGNITION 2012年第1期45卷 570-577页

作者： Chatzis, Sotirios P. Demiris, Yiannis Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn London SW7 2AZ England

Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN) training, with an RNN (the reservoir) being generated randomly, and only a readout being trained using a simple, computationally efficient algorithm. ESNs have greatly facilitated the practical application of RNNs, outperforming classical approaches on a number of benchmark tasks. This paper studies the formulation of a class of copula-based semiparametric models for sequential data modeling, characterized by nonparametric marginal distributions modeled by postulating suitable echo state networks, and parametric copula functions that help capture all the scale-free temporal dependence of the modeled processes. We provide a simple algorithm for the data-driven estimation of the marginal distribution and the copula parameters of our model under the maximum-likelihood framework. We exhibit the merits of our approach by considering a number of applications;as we show, our method offers a significant enhancement in the dynamical data modeling capabilities of ESNs, without significant compromises in the algorithm's computational efficiency. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Copula Echo state network sequential data modeling Maximum-likelihood

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Deep Heterogeneous Autoencoders for Collaborative Filtering 18

Deep Heterogeneous Autoencoders for Collaborative Filtering

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18th IEEE International Conference on data Mining Workshops (ICDMW)

作者： Li, Tianyu Ma, Yukun Xu, Jiu Stenger, Bjorn Liu, Chen Hirate, Yu Rakuten Inst Technol Tokyo Japan Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781538691595

This paper leverages heterogeneous auxiliary information to address the data sparsity problem of recommender systems. We propose a model that learns a shared feature space from heterogeneous data, such as item descriptions, product tags and online purchase history, to obtain better predictions. Our model consists of autoencoders, not only for numerical and categorical data, but also for sequential data, which enables capturing user tastes, item characteristics and the recent dynamics of user preference. We learn the autoencoder architecture for each data source independently in order to better model their statistical properties. Our evaluation on two MovieLens datasets and an e-commerce dataset shows that mean average precision and recall improve over state-of-the-art methods.

关键词： Deep Autoencoder Heterogeneous data Shared Representation sequential data modeling Collaborative Filtering

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Hidden Markov Models with Nonelliptically Contoured State Densities

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2010年第12期32卷 2297-2304页

作者： Chatzis, Sotirios P. Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn Intelligent Syst & Networks Grp London SW7 2BT England

Hidden Markov models (HMMs) are a popular approach for modeling sequential data comprising continuous attributes. In such applications, the observation emission densities of the HMM hidden states are typically modeled by means of elliptically contoured distributions, usually multivariate Gaussian or Student's-t densities. However, elliptically contoured distributions cannot sufficiently model heavy-tailed or skewed populations which are typical in many fields, such as the financial and the communication signal processing domain. Employing finite mixtures of such elliptically contoured distributions to model the HMM state densities is a common approach for the amelioration of these issues. Nevertheless, the nature of the modeled data often requires postulation of a large number of mixture components for each HMM state, which might have a negative effect on both model efficiency and the training data set's size required to avoid overfitting. To resolve these issues, in this paper, we advocate for the utilization of a nonelliptically contoured distribution, the multivariate normal inverse Gaussian (MNIG) distribution, for modeling the observation densities of HMMs. As we experimentally demonstrate, our selection allows for more effective modeling of skewed and heavy-tailed populations in a simple and computationally efficient manner.

关键词： Hidden Markov models multivariate normal inverse Gaussian (MNIG) distribution expectation-maximization sequential data modeling

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