Kernel ridge regression using truncated newton method

Maalouf, Maher; Homouz, Dirar

doi:10.1016/j.knosys.2014.08.012

Cited by 28 publications

(22 citation statements)

References 11 publications

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“…where λ corresponds to a positive regularization parameter value that is introduced to avoid overfitting [35] and I N is the identity matrix of size NxN.…”

Section: The Low Pressure Rangementioning

confidence: 99%

“…Although α is obtained from the solution of a linear system, the fact that matrix K + λI N can be very large and dense renders the solution for α in Equation 7as a very slow process with time complexity of O(n 3 ) [35]. To treat that issue, one should revert to iterative linear systems solving methods such as the Conjugate Gradient (CG), and placing a threshold on the number of iterations leads to what is called the truncated Newton.…”

Section: The Low Pressure Rangementioning

confidence: 99%

“…To treat that issue, one should revert to iterative linear systems solving methods such as the Conjugate Gradient (CG), and placing a threshold on the number of iterations leads to what is called the truncated Newton. Maalouf and Homouz [35] combined KRR with the truncated Newton method and developed a TR-KRR algorithm that is very fast to train. Interested readers should refer to Maalouf and Homouz [35] for a detailed description of TR-KRR.…”

Section: The Low Pressure Rangementioning

confidence: 99%

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An Efficient Method to Predict Compressibility Factor of Natural Gas Streams

et al. 2019

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The gas compressibility factor, also known as the deviation or Z-factor, is one of the most important parameters in the petroleum and chemical industries involving natural gas, as it is directly related to the density of a gas stream, hence its flow rate and isothermal compressibility. Obtaining accurate values of the Z-factor for gas mixtures of hydrocarbons is challenging due to the fact that natural gas is a multicomponent, non-ideal system. Traditionally, the process of estimating the Z-factor involved simple empirical correlations, which often yielded weak results either due to their limited accuracy or due to calculation convergence difficulties. The purpose of this study is to apply a hybrid modeling technique that combines the kernel ridge regression method, in the form of the recently developed Truncated Regularized Kernel Ridge Regression (TR-KRR) algorithm, in conjunction with a simple linear-quadratic interpolation scheme to estimate the Z-factor. The model is developed using a dataset consisting of 5616 data points taken directly from the Standing–Katz chart and validated using the ten-fold cross-validation technique. Results demonstrate an average absolute relative prediction error of 0.04%, whereas the maximum absolute and relative error at near critical conditions are less than 0.01 and 2%, respectively. Most importantly, the obtained results indicate smooth, physically sound predictions of gas compressibility. The developed model can be utilized for the direct calculation of the Z-factor of any hydrocarbon mixture, even in the presence of impurities, such as N 2 , CO 2 , and H 2 S, at a pressure and temperature range that fully covers all upstream operations and most of the downstream ones. The model accuracy combined with the guaranteed continuity of the Z-factor derivatives with respect to pressure and temperature renders it as the perfect tool to predict gas density in all petroleum engineering applications. Such applications include, but are not limited to, hydrocarbon reserves estimation, oil and gas reservoir modeling, fluid flow in the wellbore, the pipeline system, and the surface processing equipment.

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“…where λ corresponds to a positive regularization parameter value that is introduced to avoid overfitting [35] and I N is the identity matrix of size NxN.…”

Section: The Low Pressure Rangementioning

confidence: 99%

Section: The Low Pressure Rangementioning

confidence: 99%

Section: The Low Pressure Rangementioning

confidence: 99%

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An Efficient Method to Predict Compressibility Factor of Natural Gas Streams

et al. 2019

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“…Despite this fact, these kernel-based methods involve the solution of a quadratic optimization problem, which is computationally expensive. Apart of these kernel-based methods, KRR-based models [15] optimize the problem rapidly in a non-iterative way by solving a linear systems.Therefore, KRR-based models [15,16,17,18,19] have received quite attention by researchers for solving various types of problems viz., regression, binary, multi-class etc. In recent years, various KRR-based one-class classifiers have been developed and exhibited better performance compared to various state-of-the-art one-class classifiers.…”

mentioning

confidence: 99%

“…Therefore, KRR-based models [15,16,17,18,19] have received quite attention by researchers for solving various types of problems viz., regression, binary, multi-class etc. In recent years, various KRR-based one-class classifiers have been developed and exhibited better performance compared to various state-of-the-art one-class classifiers.…”

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confidence: 99%

Minimum Variance-Embedded Multi-layer Kernel Ridge Regression for One-class Classification

Gautam

Tiwari

Iosifidis

2018

2018 IEEE Symposium Series on Computational Intelligence (SSCI)

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In this paper, a multi-layer architecture (in a hierarchical fashion) by stacking various Kernel Ridge Regression (KRR) based Auto-Encoder for one-class classification is proposed and is referred as MKOC. MKOC has many layers of Auto-Encoders to project the input features into new feature space and the last layer is regression based one class classifier. The Auto-Encoders use an unsupervised approach of learning and the final layer uses semi-supervised (trained by only positive samples) approach of learning. The proposed MKOC is experimentally evaluated on 15 publicly available benchmark datasets. Experimental results verify the effectiveness of the proposed approach over 11 existing state-of-the-art kernel-based one-class classifiers. Friedman test is also performed to verify the statistical significance of the claim of the superiority of the proposed one-class classifiers over the existing state-of-the-art methods. description [5], self-organizing map data description [5], Auto-Encoder data descriptor [11] etc. Whereas, the kernelbased one-class classifier approaches are support vector data description [12], one-class support vector machine[13], kernel principal component analysis based data description [14] etc. However, kernel-based methods have been shown to outperform non-kernel-based methods in the literature [1,5]. Despite this fact, these kernel-based methods involve the solution of a quadratic optimization problem, which is computationally expensive. Apart of these kernel-based methods, KRR-based models [15] optimize the problem rapidly in a non-iterative way by solving a linear systems.Therefore, KRR-based models [15,16,17,18,19] have received quite attention by researchers for solving various types of problems viz., regression, binary, multi-class etc. In recent years, various KRR-based one-class classifiers have been developed and exhibited better performance compared to various state-of-the-art one-class classifiers. Overall, the KRR-based one-class classifiers can be divided into two types, namely, (i) without Graph-Embedding (ii) with Graph-Embedding. For 'without Graph-Embedding', two types of architectures have been explored for OCC. One is KRR-based single output node architecture [20], and other is KRR-based Auto-Encoder architecture [21]. For 'with Graph-Embedding', Iosifidis et al.[22] presented local and global structure. Different types of Laplacian Graphs are employed for local (i.e., Local Linear Embedding, Laplacian Eigenmaps etc.) and global (linear discriminant analysis and clustering-based discriminant analysis etc.) Graph-embedding. Later, global variance-based Graph-Embedding has been extended in order to exploit class variance and sub-class variance information for face verification task by Mygdalis et al.[23]. All the above-mentioned KRR-based one-class classifiers employ a single-layered architectures.Over the last decade, stacked Auto-encoder based multi-layer architectures have received quite attention by researchers for multi-class or binary class classification tasks [24,...

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Predicting photoresist sensitivity using machine learning

Ghule,

Kim,

Jang

2023

Bulletin Korean Chem Soc

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We introduce a scheme for predicting photoresist sensitivity using machine learning (ML) work flow on the basis of previously reported experimental data. Different ML models, specifically Linear Regression, Kernel Ridge Regression, Gaussian Process Regressor, Random Forest Regressor, and Multilayer Perceptron Regressor, were evaluated to rapidly identify the best sensitivity prediction model. The experiment was carried out on the Google Colab platform using the Materials Simulation Toolkit for Machine Learning and Sci‐kit Learn. Different ensemble models were utilized without splitting the dataset to determine the prediction accuracy of the ML models. The hyperparameter optimization was established with a 70/30 ratio, followed by a K‐Fold cross‐validation to improve the model prediction performance. The optimized ML model showed a prediction performance of R2 = 0.83, RMSE = 10.53, and MARE = 0.68. Hence, by optimizing the hyperparameters used in the ML model, the sensitivity of the photoresist materials can be predicted with improved prediction performance.

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Kernel ridge regression using truncated newton method

Cited by 28 publications

References 11 publications

An Efficient Method to Predict Compressibility Factor of Natural Gas Streams

An Efficient Method to Predict Compressibility Factor of Natural Gas Streams

Minimum Variance-Embedded Multi-layer Kernel Ridge Regression for One-class Classification

Predicting photoresist sensitivity using machine learning

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