Kernel Nonparametric Weighted Feature Extraction for Classification

Kuo, Bor‐Chen; Li, Cheng‐Hsuan

doi:10.1007/11589990_59

Cited by 23 publications

(29 citation statements)

References 6 publications

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“…In MFLDA as distinct from LDA, the total scatter matrix is used instead of the withinclass scatter matrix and class signatures are used as mean values of classes. Kuo and Landgrebe (Kuo & Landgrebe, 2004) propose a nonparametric weighted feature extraction method (NWFE). In this approach, different weights are computed for every sample.…”

Section: Introductionmentioning

confidence: 99%

“…The dimension of the hyperspectral data is reduced by using CGLDA. Kuo and Landgrebe emphasize the use of local information to improve in the discriminant analysis for feature extraction (Kuo & Landgrebe, 2004). Advantages of the usage of not only global pattern information, but also local pattern information are examined in hyperspectral image processing.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral dimension reduction using global and local information based linear discriminant analysis

Sakarya

2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Hyperspectral image classification has become an important research topic in remote sensing. Because of high dimensional data, a special attention is needed dealing with spectral data; and thus, one of the research topics in hyperspectral image classification is dimension reduction. In this paper, a dimension reduction approach is presented for classification on hyperspectral images. Advantages of the usage of not only global pattern information, but also local pattern information are examined in hyperspectral image processing. In addition, not only tuning the parameters, but also an experimental analysis of the distribution of the hyperspectral data is demonstrated. Therefore, how global or local pattern variations play an important role in classification is examined. According to the experimental outcomes, the promising results are obtained for classification on hyperspectral images.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hyperspectral dimension reduction using global and local information based linear discriminant analysis

Sakarya

2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The Fisher discriminant [27] is used to rank the features based on the observations of the various samples. Feature extraction for high-dimensional data is also done using the nonparametric [28] methods. Here the rank is given for the entire samples in the dataset.…”

Section: International Journal Of Applied Information Systems (Ijais)mentioning

confidence: 99%

Software Fault Detection using Honey Bee Optimization

Asir¹,

Escalin²,

Jebamalar³

2016

IJAIS

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The recent developments in the software technology assist humanities in various fields including engineering, technology, management, medical science, research, education, banking etc. Fault identification is a crucial one to the software testing professionals since a huge number of tests are carried out to identify the level of the defect. Therefore the machine learning algorithms are employed to develop software fault detection model in order to predict the fault in the software. The irrelevant and redundant test data reduces the accuracy of fault detection model. The accuracy of the fault detection model highly depends on the number of significant relevant test data. Therefore feature selection concept is applied to select the accurate features for developing the fault detection model. This paper proposes a method to select appropriate features with honey bee optimization technique for reducing the search space and to improve the accuracy in the software fault detection. General TermsIntroduction to the feature selection, applications of feature selection, software fault detection using feature selection, data preprocessing for improving accuracy in supervised machine learning algorithm, application of data mining, honey bee optimization in data mining approaches. KeywordsFeature selection, software fault detection algorithm, honey bee optimization, data mining approaches, machine learning algorithm, improving accuracy in classification algorithms.

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“…Yuan et al [36] utilized a hypergraph embedding model for HSI feature reduction, in which the spatial hypergraph models (SHs) are construed by selecting the K-nearest neighbors within the spatial region of the centroid pixel. Experimental results demonstrated that SH outperformed many existing feature extract methods for HSI classification, including raw spectral feature (RAW), PCA, LPP, LDA, nonparametric weighted feature extraction (NWFE) [37] and semi-supervised local discriminant analysis (SELD) [38]. However, SH is designed to learn the projection matrix for reducing the spectral feature.…”

Section: Introductionmentioning

confidence: 99%

Hypergraph Embedding for Spatial-Spectral Joint Feature Extraction in Hyperspectral Images

et al. 2017

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Abstract:The fusion of spatial and spectral information in hyperspectral images (HSIs) is useful for improving the classification accuracy. However, this approach usually results in features of higher dimension and the curse of the dimensionality problem may arise resulting from the small ratio between the number of training samples and the dimensionality of features. To ease this problem, we propose a novel algorithm for spatial-spectral feature extraction based on hypergraph embedding. Firstly, each HSI pixel is regarded as a vertex and the joint of extended morphological profiles (EMP) and spectral features is adopted as the feature associated with the vertex. A hypergraph is then constructed by the K-Nearest-Neighbor method, in which each pixel and its most K relevant pixels are linked as one hyperedge to represent the complex relationships between HSI pixels. Secondly, the hypergraph embedding model is designed to learn a low dimensional feature with the reservation of geometric structure of HSI. An adaptive hyperedge weight estimation scheme is also introduced to preserve the prominent hyperedges by the regularization constraint on the weight. Finally, the learned low-dimensional features are fed to the support vector machine (SVM) for classification. The experimental results on three benchmark hyperspectral databases are presented. They highlight the importance of spatial-spectral joint features embedding for the accurate classification of HSI data. The weight estimation is better for further improving the classification accuracy. These experimental results verify the proposed method.

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Kernel Nonparametric Weighted Feature Extraction for Classification

Cited by 23 publications

References 6 publications

Hyperspectral dimension reduction using global and local information based linear discriminant analysis

Hyperspectral dimension reduction using global and local information based linear discriminant analysis

Software Fault Detection using Honey Bee Optimization

Hypergraph Embedding for Spatial-Spectral Joint Feature Extraction in Hyperspectral Images

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