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IEEE ACCESS 2021年 9卷 132733-132745页

作者： Qi, Li Yi, Xiaoyun Yao, Lina Fang, Yixian Ren, Yuwei Shandong Normal Univ Sch Informat Sci & Engn Jinan 250353 Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Shandong Acad Sci Qilu Univ Technol Sch Math & Stat Jinan 250353 Peoples R China

To date, quality-related multivariate statistical methods are extensively used in process monitoring and have achieved admirable effects. However, most of them contain recursive processes, which result in higher time complexity and are not suitable for increasingly complex industrial processes. Therefore, this paper embeds singular value decomposition (SVD) into the kernel principal component regression (KPCR) to accomplish Quality-related process monitoring with a lower computational cost. Specifically, the kernel technique is devoted to map the original input into the higher dimensional space to boost the nonlinear ability of the principal component regression (PCR), and then the KPCR is employed to capture the correlation between the input kernel matrix and the output matrix. At the same time, the kernelized input space is decomposed into two orthogonal quality-related and quality-unrelated spaces by SVD, and the statistics of the two spaces are calculated to detect the faults respectively. Compared with other multivariate statistical methods, it has the following advantages: 1) A quality-related kernel principal component analysis (QR-KPCR) algorithm is proposed. 2) Compared with partial least squares method, the recursive process is omitted and the training time is shortened. 3) The model is more concise and the fault detection process is faster. 4) By contrast with other multivariate statistical process monitoring, it has a higher fault detection rate. Experimental results on a widespread example and an industry benchmark verify the effectiveness and reliability of the proposed method.

关键词： kernel Fault detection Process monitoring Training principal component analysis Data models Time complexity Process monitoring fault detection kernel principal component regression multivariate statistical process monitoring

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Batch-to-Batch Iterative Learning Control for End-Point Qualities Based on kernel principal component regression Model

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JOURNAL OF COMPUTERS 2013年第12期8卷 3184-3190页

作者： Li, Ganping Nanchang Univ Sch Informat Engn Nanchang Jiangxi Peoples R China

A batch-to-batch model-based iterative learning control (ILC) strategy for the end-point product quality control in batch processes is proposed in this paper. A nonlinear model for end-point product quality is developed from process operating data using kernel principal component regression (KPCR). The ILC algorithm is derived to calculate the control policy by linearizing the KPCR model around the nominal trajectories and minimising a quadratic objective function concerning the end-point product quality. To overcome the detrimental effects of unknown process variations or disturbances, it is proposed in the paper that the KPCR model should be updated in a batchwise manner by removing the earliest batch data from the training data set and adding the latest batch data to the training data set. The ILC based on updated KPCR model shows adaptability for process variations or disturbances when applied to a simulated batch polymerization process. Comparisons between KPCR model and principal component regression (PCR) model based ILCs are also made in the simulations.

关键词： Iterative Learning Control kernel principal component regression Batch Process

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Parallel Software for Million-scale Exact kernel regression 23

Parallel Software for Million-scale Exact Kernel Regression

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37th ACM International Conference on Supercomputing (ACM ICS)

作者： Chen, Yu Skon, Lucca McCombs, James R. Liu, Zhenming Stathopoulos, Andreas William & Mary Williamsburg VA 23185 USA Indiana Univ Bloomington IN USA

ISBN: (纸本)9798400700569

We present the design and the implementation of a kernel principal component regression software that handles training datasets with a million or more observations. kernel regressions are nonlinear and interpretable models that have wide downstream applications, and are shown to have a close connection to deep learning. Nevertheless, the exact regression of large-scale kernel models using currently available software has been notoriously difficult because it is both compute and memory intensive and it requires extensive tuning of hyperparameters. While in computational science distributed computing and iterative methods have been a mainstay of large scale software, they have not been widely adopted in kernel learning. Our software leverages existing high performance computing (HPC) techniques and develops new ones that address cross-cutting constraints between HPC and learning algorithms. It integrates three major components: (a) a state-of-the-art parallel eigenvalue iterative solver, (b) a block matrix-vector multiplication routine that employs both multi-threading and distributed memory parallelism and can be performed on-the-fly under limited memory, and (c) a software pipeline consisting of Python front-ends that control the HPC backbone and the hyperparameter optimization through a boosting optimizer. We perform feasibility studies by running the entire ImageNet dataset and a large asset pricing dataset.

关键词： kernel principal component regression SVD parallel algorithm classification machine learning software tools boosting

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A note on kernel principal component regression

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Computational Mathematics and Modeling 2012年第3期23卷 350-367页

作者： Wibowo, Antoni Yamamoto, Yoshitsugu Faculty of Computer Science and Information Systems Universiti Teknologi Malaysia Johor Malaysia Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba Japan

kernel principal component regression (KPCR) was studied by Rosipal et al. [18, 19, 20], Hoegaerts et al. [7], and Jade et al. [8]. However, KPCR still encounters theoretical difficulties in the procedure for constructing KPCR and in the choice rule for the retained number of principal components. In this paper, we revise the method of KPCR to overcome the difficulties. The performance of the revised method is compared to linear regression, nonlinear regression based on Gompertz function, and nonparametric Nadaraya-Watson regression, and gives better results than those of the three methods. © 2012 Springer Science+Business Media, Inc.

关键词： kernel principal component analysis kernel principal component regression multicollinearity Nonlinear regression analysis

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Predicting dissolved oxygen concentration using kernel regression modeling approaches with nonlinear hydro-chemical data

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2014年第5期186卷 2749-2765页

作者： Singh, Kunwar P. Gupta, Shikha Rai, Premanjali Acad Sci & Innovat Res New Delhi 110001 India CSIR Indian Inst Toxicol Res Div Environm Chem Lucknow 226001 Uttar Pradesh India

kernel function-based regression models were constructed and applied to a nonlinear hydrochemical dataset pertaining to surface water for predicting the dissolved oxygen levels. Initial features were selected using nonlinear approach. Nonlinearity in the data was tested using BDS statistics, which revealed the data with nonlinear structure. kernel ridge regression, kernel principal component regression, kernel partial least squares regression, and support vector regression models were developed using the Gaussian kernel function and their generalization and predictive abilities were compared in terms of several statistical parameters. Model parameters were optimized using the cross-validation procedure. The proposed kernel regression methods successfully captured the nonlinear features of the original data by transforming it to a high dimensional feature space using the kernel function. Performance of all the kernel-based modeling methods used here were comparable both in terms of predictive and generalization abilities. Values of the performance criteria parameters suggested for the adequacy of the constructed models to fit the nonlinear data and their good predictive capabilities.

关键词： kernel ridge regression kernel principal component regression kernel partial least squares regression Support vector regression Nonlinearity

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Spectral Mapping Using kernel principal components regression for Voice Conversion

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ARCHIVES OF ACOUSTICS 2013年第1期38卷 39-45页

作者： Song, Peng Zhao, Li Bao, Yongqiang Southeast Univ Minist Educ Key Lab Underwater Acoust Signal Proc Nanjing 210096 Jiangsu Peoples R China Nanjing Inst Technol Sch Commun Engn Nanjing 211167 Jiangsu Peoples R China

The Gaussian mixture model (GMM) method is popular and efficient for voice conversion (VC), but it is often subject to overfitting. In this paper, the principal component regression (PCR) method is adopted for the spectral mapping between source speech and target speech, and the numbers of principal components are adjusted properly to prevent the overfitting. Then, in order to better model the nonlinear relationships between the source speech and target speech, the kernel principal component regression (KPCR) method is also proposed. Moreover, a KPCR combined with GMM method is further proposed to improve the accuracy of conversion. In addition, the discontinuity and oversmoothing problems of the traditional GMM method are also addressed. On the one hand, in order to solve the discontinuity problem, the adaptive median filter is adopted to smooth the posterior probabilities. On the other hand, the two mixture components with higher posterior probabilities for each frame are chosen for VC to reduce the oversmoothing problem. Finally, the objective and subjective experiments are carried out, and the results demonstrate that the proposed approach shows greatly better performance than the GMM method. In the objective tests, the proposed method shows lower cepstral distances and higher identification rates than the GMM method. While in the subjective tests, the proposed method obtains higher scores of preference and perceptual quality.

关键词： spectral mapping overfitting oversmoothing discontinuity kernel principal component regression

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kernel auto-associator from kernel principal component autoregression with application to face recognition

Kernel auto-associator from kernel principal component autor...

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International Conference on Computational Intelligence for Modelling, Control and Automation/International Conference on Intelligent Agents Web Technologies and International Commerce

作者： Zhang, Bai-ling Gupta, Gopal Victoria Univ Technol Sch Comp Sci & Math Melbourne Vic 3011 Australia Monash Univ Sch Com Sci & Software Engn Melbourne FL 3800 USA

ISBN: (纸本)0769525040

Based on the kernel principal component regression (KPCR) recently proposed in the literature, a new kernel auto-associator (KAA) model is proposed for classification and novelty detection. For face recognition problem, KAA model can efficiently characterize each subject thus offering a good recognition performance. Steming from the principal component regression (PCR), a simple technique using principal components as a subset selection method in regression, KPCR,selects a subset of the principal components from the kernel space for the response variables to regress. As an extension of KPCR, a kernel autoassociator model can be built from autoregression by first extracting features in the kernel space and then performing ordinary least square reconstruction from the selected features. To demonstrate the performance of the proposed KAA model, face recognition is studied as a benchmark example, with a modular scheme which-is consist of two stages. The first stage is a preprocessing by a multilevel two-dimensional (2D) discrete wavelet transform. The second stage is a subject-specific KAA structure, which means each subject is assigned a KAA model for coding the corresponding visual information. Experiments on several well-known face datasets demonstrated high recognition accuracies by just using a few of the kernel principal components.

关键词： kernel auto-associator face recognition kernel principal component regression wavelet transform kernel methods

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