Extended Averaged Learning Subspace Method for Hyperspectral Data Classification

Eigenvector-based spatial filtering is one of the often used approaches to model spatial autocorrelation among the observations or errors in a regression model. In this approach, a subset of eigenvectors extracted from a modified spatial weight matrix is added to the model as explanatory variables. The subset is typically specified via the selection procedure of the forward stepwise model, but it is disappointingly slow when the observations n take a large number. Hence, as a complement or alternative, the present article proposes the use of the least absolute shrinkage and selection operator (LASSO) to select the eigenvectors. The LASSO model selection procedure was applied to the well-known Boston housing data set and simulation data set, and its performance was compared with the stepwise procedure. The obtained results suggest that the LASSO procedure is fairly fast compared with the stepwise procedure, and can select eigenvectors effectively even if the data set is relatively large (n = 10 4 ), to which the forward stepwise procedure is not easy to apply.

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“…Furthermore, we are now trying to develop a more efficient approach to the eigenvector selection based on the subspace method (Bagan et al. ).…”

Section: Discussionmentioning

confidence: 99%

Application ofLASSOto the Eigenvector Selection Problem in Eigenvector‐based Spatial Filtering

Seya

Murakami

Tsutsumi

et al. 2014

Geographical Analysis

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“…Also, applying the LASSO based eigenvector selection approach to the other models such as generalized linear model (GLM) is an important remains researches. Furthermore, we are now trying to develop a more efficient approach for the eigenvector selection based on subspace method (Bagan et al 2009).…”

Section: Discussionmentioning

confidence: 99%

Application of Lasso to the Eigenvector Selection Problem in Eigenvector Based Spatial Filtering

Seya

2013

SSRN Journal

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Eigenvector based spatial filtering is one of the well-used approaches to model spatial autocorrelation among the observations or errors in a regression model. In this approach, subset of eigenvectors extracted from a modified spatial weight matrix is added to the model as explanatory variables. The subset is typically specified via the forward stepwise model selection procedure, but it is disappointingly slow when the number of observations n takes a large number. Hence as a complement or alternative, the present paper proposes the use of the least absolute shrinkage and selection operator (LASSO) to select the eigenvectors. The LASSO model selection procedure is applied to the well-known Boston housing dataset and simulation dataset, and its performance is compared with the stepwise procedure. The obtained results suggest that the LASSO procedure is fairly fast compared to the stepwise procedure, and can select eigenvectors effectively even if dataset is relatively large (n = 10 4), to which the forward stepwise procedure is not easy to apply.

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“…In order to make the feature values in the same order of magnitude and improve the convergence speed of the model, we normalize the extracted features ( Bagan et al, 2009 ). The five feature values are mapped to [−1, 1] and the normalized feature values are taken as input variables of the BPNN model in this paper.…”

Section: The Process Of the Proposed Algorithmmentioning

confidence: 99%

Ultrasonic based concrete defects identification via wavelet packet transform and GA-BP neural network

Zhao

Zheng

et al. 2021

PeerJ Computer Science

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Concrete is the main material in building. Since its poor structural integrity may cause accidents, it is significant to detect defects in concrete. However, it is a challenging topic as the unevenness of concrete would lead to the complex dynamics with uncertainties in the ultrasonic diagnosis of defects. Note that the detection results mainly depend on the direct parameters, e.g., the time of travel through the concrete. The current diagnosis accuracy and intelligence level are difficult to meet the design requirement for automatic and increasingly high-performance demands. To solve the mentioned problems, our contribution of this paper can be summarized as establishing a diagnosis model based on the GA-BPNN method and ultrasonic information extracted that helps engineers identify concrete defects. Potentially, the application of this model helps to improve the working efficiency, diagnostic accuracy and automation level of ultrasonic testing instruments. In particular, we propose a simple and effective signal recognition method for small-size concrete hole defects. This method can be divided into two parts: (1) signal effective information extraction based on wavelet packet transform (WPT), where mean value, standard deviation, kurtosis coefficient, skewness coefficient and energy ratio are utilized as features to characterize the detection signals based on the analysis of the main frequency node of the signals, and (2) defect signal recognition based on GA optimized back propagation neural network (GA-BPNN), where the cross-validation method has been used for the stochastic division of the signal dataset and it leads to the BPNN recognition model with small bias. Finally, we implement this method on 150 detection signal data which are obtained by the ultrasonic testing system with 50 kHz working frequency. The experimental test block is a C30 class concrete block with 5, 7, and 9 mm penetrating holes. The information of the experimental environment, algorithmic parameters setting and signal processing procedure are described in detail. The average recognition accuracy is 91.33% for the identification of small size concrete defects according to experimental results, which verifies the feasibility and efficiency.

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Extended Averaged Learning Subspace Method for Hyperspectral Data Classification

Cited by 9 publications

References 38 publications

Application ofLASSOto the Eigenvector Selection Problem in Eigenvector‐based Spatial Filtering

Application ofLASSOto the Eigenvector Selection Problem in Eigenvector‐based Spatial Filtering

Application of Lasso to the Eigenvector Selection Problem in Eigenvector Based Spatial Filtering

Ultrasonic based concrete defects identification via wavelet packet transform and GA-BP neural network

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