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Safety exploration using gaussian process classification for uncertain systems☆

RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 256卷

作者： Wang, Ke Menon, Prathyush P. Veenman, Joost Bennani, Samir Univ Exeter Fac Environm Sci & Econ Exeter EX4 4PY England SENER Aerosp Madrid 28760 Spain European Space Agcy NL-2201 Noordwijk Netherlands

In this paper, a novel method for identifying safe and unsafe regions of the system's uncertain parameter space is proposed. Fora given set of performance requirements, such estimation can be obtained by means of binary classification in which uncertain parameters are classified as either safe or unsafe in the sense that the given performance requirements are met or not. Hence, using gaussian process classification it is possible to obtain (non-convex) safe and unsafe regions supported by minimum confidence levels of the corresponding estimations. We adopt active learning to update the gaussian process classification model and to make more accurate predictions by selecting informative observations sequentially. The effectiveness of the proposed algorithm is demonstrated on various illustrative examples.

关键词： Safe and unsafe regions Parametric uncertainty gaussian process classification Active learning

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gaussian process classification for prediction of in-hospital mortality among preterm infants

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NEUROCOMPUTING 2018年 298卷 134-141页

作者： Rinta-Koski, Olli-Pekka Sarkka, Simo Hollmen, Jaakko Leskinen, Markus Andersson, Sture Aalto Univ Dept Comp Sci POB 15400 FI-00076 Aalto Finland Aalto Univ Dept Elect Engn & Automat POB 12200 FI-00076 Aalto Finland Univ Helsinki POB 140 FI-00029 Helsinki Finland Helsinki Univ Hosp POB 140 FI-00029 Helsinki Finland

We present a method for predicting preterm infant in-hospital mortality using Bayesian gaussian process classification. We combined features extracted from sensor measurements, made during the first 72 h of care for 598 Very Low Birth Weight infants of birth weight < 1500 g, with standard clinical features calculated on arrival at the Neonatal Intensive Care Unit. Time periods of 12, 18, 24, 36, 48, and 72 h were evaluated. We achieved a classification result with area under the receiver operating characteristic curve of 0.948, which is in excess of the results achieved by using the clinical standard SNAP-II and SNAPPE-II scores. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Time series prediction gaussian process classification Very low birth weight infants Neonatal intensive care

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gaussian process classification for variable fidelity data

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NEUROCOMPUTING 2020年 397卷 345-355页

作者： Klyuchnikov, Nikita Burnaev, Evgeny Skolkovo Inst Sci & Technol Skolkovo Innovat Ctr Bldg 3 Moscow 143026 Russia

In this paper we address a classification problem where two sources of labels with different levels of fidelity are available. Our approach is to combine data from both sources by applying a co-kriging schema on latent functions, which allows the model to account item-dependent labeling discrepancy. We provide an extension of Laplace inference for gaussian process classification, that takes into account multi-fidelity data. We evaluate the proposed method on real and synthetic datasets and show that it is more resistant to different levels of discrepancy between sources than other approaches for data fusion. Our method can provide accuracy/cost trade-offfor a number of practical tasks such as crowd-sourced data annotation and feasibility regions construction in engineering design. (C) 2019 Elsevier B.V. All rights reserved.

关键词： gaussian process classification Variable fidelity data Laplace inference

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gaussian process classification Using Posterior Linearization

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IEEE SIGNAL processING LETTERS 2019年第5期26卷 735-739页

作者： Garcia-Fernandez, Angel F. Tronarp, Filip Sarkka, Simo Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England Univ Antonio de Nebrija ARIES Res Ctr Madrid 28015 Spain Aalto Univ Dept Elect Engn & Automat Espoo 02150 Finland

This letter proposes a new algorithm for gaussian process classification based on posterior linearization (PL). In PL, a gaussian approximation to the posterior density is obtained iteratively using the best possible linearization of the conditional mean of the labels and accounting for the linearization error. PL has some theoretical advantages over expectation propagation (EP): all calculated covariance matrices are positive definite and there is a local convergence theorem. In experimental data, PI, has better performance than EP with the noisy threshold likelihood and the parallel implementation of the algorithms.

关键词： gaussian process classification posterior linearization Bayesian inference simultaneous wireless information and power transfer two-way relaying energy efficiency

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gaussian process classification using automatic relevance determination for SAR target recognition

Gaussian process classification using automatic relevance de...

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Conference on Image and Signal processing for Remote Sensing XVI

作者： Zhang, Xiangrong Gou, Limin Hou, Biao Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Inst Intelligent Informat Proc Xian 710071 Peoples R China

ISBN: (纸本)9780819483478

In this paper, a Synthetic Aperture Radar Automatic Target Recognition approach based on gaussian process (GP) classification is proposed. It adopts kernel principal component analysis to extract sample features and implements target recognition by using GP classification with automatic relevance determination (ARD) function. Compared with k-Nearest Neighbor, Naive Bayes classifier and Support Vector Machine, GP with ARD has the advantage of automatic model selection and hyper-parameter optimization. The experiments on UCI datasets and MSTAR database show that our algorithm is self-tuning and has better recognition accuracy as well.

关键词： gaussian process classification automatic relevance determination SAR target recognition

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gaussian process classification as Metric Learning for Forensic Writer Identification 13

Gaussian Process Classification as Metric Learning for Foren...

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13th IAPR International Workshop on Document Analysis Systems (DAS)

作者： Wahlberg, Fredrik Univ Gavle Div Humanities Gavle Sweden

ISBN: (纸本)9781538633465

In this paper, a statistical machine learning approach for constructing a metric separating unseen writer hands, is proposed. An unsupervised feature learning approach, based on dense contour descriptor sampling, was combined with a novel way of learning a general space for clustering writer hands, in a forensic setting. The metric learning inference was based on multiclass gaussian process classification. Using the popular datasets IAM and CVL combined, the evaluation was performed on close to 1000 writer hands. This paper builds on earlier work from our group on building a system for estimating the production dates of medieval manuscripts, and act as a foundation for future use of writer identification techniques on our historical data.

关键词： component writer identification document analysis digital paleography unsupervised feature learning gaussian process classification

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Human action recognition by feature-reduced gaussian process classification

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PATTERN RECOGNITION LETTERS 2009年第12期30卷 1059-1066页

作者： Zhou, Hang Wang, Liang Suter, David Monash Univ Dept Elect & Comp Syst Engn Clayton Vic 3800 Australia Univ Melbourne Dept Comp Sci & Software Engn Melbourne Vic 3010 Australia

This paper presents a spectral analysis-based feature-reduced gaussian processes (GP) classification approach to recognition of articulated and deformable human actions from image sequences. Using Tensor Subspace Analysis (TSA), space-time human silhouettes extracted from action sequences are transformed to a low dimensional multivariate time series, from which structure-based statistical features are extracted to summarize the action properties. GP classification, based oil spectrally reduced features, is then applied to learn and predict action categories. Experimental results on two real-world state-of-the-art datasets show that the GP classification outperforms a Support Vector Machine (SVM). In particular, spectral feature reduction can effectively eliminate the inconsistent features, while leaving performance undiminished. Moreover, compared with Automatic Relevance Determination (ARD), the spectral way for feature reduction is more efficient. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Human action recognition Characteristic-based descriptor gaussian process classification Spectral feature reduction

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A novel reliability analysis method based on gaussian process classification for structures with discontinuous response

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STRUCTURAL ENGINEERING AND MECHANICS 2020年第6期75卷 771-784页

作者： Zhang, Yibo Sun, Zhili Yan, Yutao Yu, Zhenliang Wang, Jian Northeastern Univ Sch Mech Engn & Automat Wenhua Rd Shenyang 110819 Liaoning Peoples R China

Reliability analysis techniques combining with various surrogate models have attracted increasing attention because of their accuracy and great efficiency. However, they primarily focus on the structures with continuous response, while very rare researches on the reliability analysis for structures with discontinuous response are carried out. Furthermore, existing adaptive reliability analysis methods based on importance sampling (IS) still have some intractable defects when dealing with small failure probability, and there is no related research on reliability analysis for structures involving discontinuous response and small failure probability. Therefore, this paper proposes a novel reliability analysis method called AGPC-IS for such structures, which combines adaptive gaussian process classification (GPC) and adaptive-kernel-density-estimation-based IS. In AGPC-IS, an efficient adaptive strategy for design of experiments (DoE), taking into consideration the classification uncertainty, the sampling uniformity and the regional classification accuracy improvement, is developed with the purpose of improving the accuracy of gaussian process classifier. The adaptive kernel density estimation is introduced for constructing the quasi-optimal density function of IS. In addition, a novel and more precise stopping criterion is also developed from the perspective of the stability of failure probability estimation. The efficiency, superiority and practicability of AGPC-IS are verified by three examples.

关键词： reliability analysis discontinuous response gaussian process classification small failure probability importance sampling

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Bayesian gaussian process classification with the EM-EP algorithm

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2006年第12期28卷 1948-1959页

作者： Kim, Hyun-Chul Ghahramani, Zoubin Pohang Univ Sci & Technol Dept Ind & Management Engn Pohang 790784 South Korea Univ Cambridge Dept Engn Cambridge CB2 1PZ England

gaussian process classifiers (GPCs) are Bayesian probabilistic kernel classifiers. In GPCs, the probability of belonging to a certain class at an input location is monotonically related to the value of some latent function at that location. Starting from a gaussian process prior over this latent function, data are used to infer both the posterior over the latent function and the values of hyperparameters to determine various aspects of the function. Recently, the expectation propagation (EP) approach has been proposed to infer the posterior over the latent function. Based on this work, we present an approximate EM algorithm, the EM-EP algorithm, to learn both the latent function and the hyperparameters. This algorithm is found to converge in practice and provides an efficient Bayesian framework for learning hyperparameters of the kernel. A multiclass extension of the EM-EP algorithm for GPCs is also derived. In the experimental results, the EM-EP algorithms are as good or better than other methods for GPCs or Support Vector Machines (SVMs) with cross-validation.

关键词： gaussian process classification Bayesian methods kernel methods expectation propagation EM-EP algorithm

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Spectrum-Based Kernel Length Estimation for gaussian process classification

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IEEE TRANSACTIONS ON CYBERNETICS 2014年第6期44卷 805-816页

作者： Wang, Liang Li, Chuan Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Max Planck Inst Informat D-66123 Saarbrucken Germany

Recent studies have shown that gaussian process (GP) classification, a discriminative supervised learning approach, has achieved competitive performance in real applications compared with most state-of-the-art supervised learning methods. However, the problem of automatic model selection in GP classification, involving the kernel function form and the corresponding parameter values (which are unknown in advance), remains a challenge. To make GP classification a more practical tool, this paper presents a novel spectrum analysis-based approach for model selection by refining the GP kernel function to match the given input data. Specifically, we target the problem of GP kernel length scale estimation. Spectrums are first calculated analytically from the kernel function itself using the autocorrelation theorem as well as being estimated numerically from the training data themselves. Then, the kernel length scale is automatically estimated by equating the two spectrum values, i.e., the kernel function spectrum equals to the estimated training data spectrum. Compared with the classical Bayesian method for kernel length scale estimation via maximizing the marginal likelihood (which is time consuming and could suffer from multiple local optima), extensive experimental results on various data sets show that our proposed method is both efficient and accurate.

关键词： Autocorrelation gaussian process classification kernel length scale estimation spectrum analysis

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