A computationally efficient scheme for feature extraction with kernel discriminant analysis

Min, Hwang-Ki; Hou, Yuexian; Park, Sangwoo; Song, Iickho

doi:10.1016/j.patcog.2015.08.021

Cited by 22 publications

(11 citation statements)

References 24 publications

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“…Linear Discriminant Analysis (LDA) was introduced by Fisher in the original article “The use of multiple measures in taxonomic problems (1936).” LDA is known as a method that obtains the linear combination of features, which can best separate between two classes of objects. KLDA is considered as a one of the nonlinear extensions of LDA, and it constructs a nonlinear discriminant mapping as a linear combination of kernel functions …”

Section: The Proposed Methodologymentioning

confidence: 99%

“…Linear Discriminant Analysis (LDA) was introduced by Fisher in the original article "The use of multiple measures in taxonomic problems (1936)." 25 LDA is known as a method that obtains the linear combination of features, which Step 2: Calculate the derivatives from this mean and store them in a matrix called B of dimensions of (M*N)…”

Section: Kernel Linear Discriminate Analysis (Klda)mentioning

confidence: 99%

“…The confusion matrix of our method on training and test images is listed in Equation 25. The element of ith row and jth column represents the classification accuracy belonging to class i, as assigned to class j after the supervised classification.…”

Section: Classification Accuracymentioning

confidence: 99%

“…Accuracy 5 ((TN 1 TP)/ (TN 1 TP 1 FN 1 FP)) * 100 (25) Where TP: true positive, the classification result is positive in presence of tumor. TN: true negative; the classification result is negative in absence of tumor.…”

Section: Classification Accuracymentioning

confidence: 99%

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Computational intelligence techniques for human brain MRI classification

El‐Dahshan

Bassiouni

2018

Int J Imaging Syst Tech

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This study proposes an image classification methodology that automatically classifies human brain magnetic resonance (MR) images. The proposed methods contain four main stages: Data acquisition, preprocessing, feature extraction, feature reduction and classification, followed by evaluation. First stage starts by collecting MRI images from Harvard and our constructed Egyptian database. Second stage starts with noise reduction in MR images. Third stage obtains the features related to MRI images, using stationary wavelet transformation. In the fourth stage, the features of MR images have been reduced using principles of component analysis and kernel linear discriminator analysis (KLDA) to the more essential features. In last stage, the classification stage, two classifiers have been developed to classify subjects as normal or abnormal MRI human images. The first classifier is based on K‐Nearest Neighbor (KNN) on Euclidean distance. The second classifier is based on Levenberg‐Marquardt (LM‐ANN). Classification accuracy of 100% for KNN and LM‐ANN classifiers has been obtained. The result shows that the proposed methodologies are robust and effective compared with other recent works.

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Section: The Proposed Methodologymentioning

confidence: 99%

Section: Kernel Linear Discriminate Analysis (Klda)mentioning

confidence: 99%

Section: Classification Accuracymentioning

confidence: 99%

Section: Classification Accuracymentioning

confidence: 99%

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Computational intelligence techniques for human brain MRI classification

El‐Dahshan

Bassiouni

2018

Int J Imaging Syst Tech

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“…Feature extraction techniques are intended to cope with the redundancy problem by selecting a subset of features that can facilitate data interpretation while reducing data storage requirements and improving prediction performance [63,64,65,66,67]. …”

Section: Electronic Nose (E-nose)mentioning

confidence: 99%

Identification of Chinese Herbal Medicines with Electronic Nose Technology: Applications and Challenges

Zhou

Luo

GholamHosseini

et al. 2017

Sensors

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This paper provides a review of the most recent works in machine olfaction as applied to the identification of Chinese Herbal Medicines (CHMs). Due to the wide variety of CHMs, the complexity of growing sources and the diverse specifications of herb components, the quality control of CHMs is a challenging issue. Much research has demonstrated that an electronic nose (E-nose) as an advanced machine olfaction system, can overcome this challenge through identification of the complex odors of CHMs. E-nose technology, with better usability, high sensitivity, real-time detection and non-destructive features has shown better performance in comparison with other analytical techniques such as gas chromatography-mass spectrometry (GC-MS). Although there has been immense development of E-nose techniques in other applications, there are limited reports on the application of E-noses for the quality control of CHMs. The aim of current study is to review practical implementation and advantages of E-noses for robust and effective odor identification of CHMs. It covers the use of E-nose technology to study the effects of growing regions, identification methods, production procedures and storage time on CHMs. Moreover, the challenges and applications of E-nose for CHM identification are investigated. Based on the advancement in E-nose technology, odor may become a new quantitative index for quality control of CHMs and drug discovery. It was also found that more research could be done in the area of odor standardization and odor reproduction for remote sensing.

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Semisupervised local preserving embedding algorithm based on maximum margin criterion for large‐scale data streams

Tan

2017

Concurrency and Computation

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Summary In the field of machine learning, feature extraction is one of the most important preprocessing in data classification for its effectiveness, and now it has attracted much extensive attention for large‐scale data stream preprocessing step, especially in the era of big data. Motivated by the advantages of unsupervised and supervised feature extraction, which are two desirable and promising characteristics for dimension reduction, a new semisupervised local preserving embedding algorithm based on maximum margin criterion (SLPE/MMC) is proposed in this paper. First, the objective functions of maximum margin criterion (MMC) and neighborhood preserving embedding (NPE) are combined to get the first objective function of SLPE/MMC. Then, in order to overcome the out‐of‐sample problem, a linear transformation is introduced to construct the second objective function. At last, the whole optimal objective function is constructed by combing the two objective functions together. The proposed algorithm has effectively taken advantage of the sample's supervised information and keeps the geometry structure and the class discrimination information of the manifold. Experiments on face datasets Yale, CMU PIE, and AR datasets are performed to evaluate the classification accuracy of SLPE/MMC. The experimental results and time complexity comparisons have demonstrated the effectiveness of the proposed method.

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A computationally efficient scheme for feature extraction with kernel discriminant analysis

Cited by 22 publications

References 24 publications

Computational intelligence techniques for human brain MRI classification

Computational intelligence techniques for human brain MRI classification

Identification of Chinese Herbal Medicines with Electronic Nose Technology: Applications and Challenges

Semisupervised local preserving embedding algorithm based on maximum margin criterion for large‐scale data streams

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