Medical text categorization using SEBLA and Kernel Discriminant Analysis

Tahir, Muhammad Atif; Khan, E.; Salem, Adel Al

doi:10.1109/wswan.2015.7210310

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…In the two equations above, represents the centroid of the global centroid and represents the k-th class centroids in the feature space [9].…”

Section: B Kernel Discriminant Analysismentioning

confidence: 99%

“…With Eigenface, a method of reducing the input dimensionality such as Principle Component Analysis (PCA) is used. PCA method converts images into a lowdimension space and performs a linear matrix transformation that finds the data variance in the projection subspace [9]. Fisherface method focuses on the variation of light direction, and facial expressions, which can be implemented by Linear Discriminant Analysis (LDA) technique.…”

Section: Introductionmentioning

confidence: 99%

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Face Recognition System Based on Kernel Discriminant Analysis, K-Nearest Neighbor and Support Vector Machine

Al-Dabagh¹,

Alhabib

Almukhtar³

2018

IJRE

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Although many methods have been implemented in the past, face recognition is still an active field of research especially after the current increased interest in security. In this paper, a face recognition system using Kernel Discriminant Analysis (KDA) and Support Vector Machine (SVM) with K-nearest neighbor (KNN) methods is presented. The kernel discriminates analysis is applied for extracting features from input images. Furthermore, SVM and KNN are employed to classify the face image based on the extracted features. This procedure is applied on each of Yale and ORL databases to evaluate the performance of the suggested system. The experimental results show that the system has a high recognition rate with accuracy up to 95.25% on the Yale database and 96% on the ORL, which are considered very good results comparing with other reported face recognition systems.

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“…In the two equations above, represents the centroid of the global centroid and represents the k-th class centroids in the feature space [9].…”

Section: B Kernel Discriminant Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Face Recognition System Based on Kernel Discriminant Analysis, K-Nearest Neighbor and Support Vector Machine

Al-Dabagh¹,

Alhabib

Almukhtar³

2018

IJRE

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show abstract

“…Kernel methods have been shown to outperform linear techniques in a number of classification, regression and structure extraction procedures inherent to modern machine learning. Relevant examples include Spectral Clustering [2,3,4,5], Principal Component Analysis [6,7,8,9], Support Vector Machines [10], Discriminant Analysis [11,12,13,14] or Canonical Correlation Analysis [15,16,17,18].…”

Section: Introductionmentioning

confidence: 99%

Asymptotic Spectral Behavior of Kernel Matrices in Complex Valued Observations

Mestre

Pereira

Gregoratti

2021

2021 IEEE Data Science and Learning Workshop (DSLW)

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The spectral behavior of kernel matrices built from complex multivariate data is established in the asymptotic regime where both the number of observations and their dimensionality increase without bound at the same rate. The result is an extension of currently available results for inner product based kernel matrices formed from real valued observations to the case where the input data is complex valued. In particular, assuming complex independent standardized Gaussian inputs and imposing certain conditions on the kernel function, it is shown that the empirical distribution of eigenvalues of this type of matrices converges almost surely to a probability measure in this asymptotic domain. Furthermore, the asymptotic spectral density can be obtained by solving a quartic polynomial equation involving its Stieltjes transform and some coefficients depending on the Hermite-like expansion of the kernel function. This is in stark contrast with the equivalent result for real valued observations, in which the underlying polynomial equation is cubic.

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Medical text categorization using SEBLA and Kernel Discriminant Analysis

Cited by 2 publications

References 15 publications

Face Recognition System Based on Kernel Discriminant Analysis, K-Nearest Neighbor and Support Vector Machine

Face Recognition System Based on Kernel Discriminant Analysis, K-Nearest Neighbor and Support Vector Machine

Asymptotic Spectral Behavior of Kernel Matrices in Complex Valued Observations

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