Plot Multivariate Menggunakan Kernel Principal Component Analysis (KPCA) dengan Fungsi Power Kernel
d'CARTESIAN, 2015 Kernel PCA merupakan PCA yang diaplikasikan pada input data yang telah ditransformasikan ke feature space. Misalkan F: Rn®F fungsi yang memetakan semua input data xiÎRn, berlaku F(xi)ÎF. Salah satu dari banyak fungsi kernel adalah power kernel. Fungsi power kernel K(xi, xj) = –|| xi – xj ||b dengan 0 < b ≤ 1.
Bawotong, Vitawati +2 more
openaire +3 more sources
EURASIP Journal on Advances in Signal Processing, 2009 Kernel principal component analysis (KPCA) is investigated for feature extraction from hyperspectral remote sensing data. Features extracted using KPCA are classified using linear support vector machines.
Mathieu Fauvel +2 more
doaj +1 more source
Thyristor State Evaluation Method Based on Kernel Principal Component Analysis
IEEE Access, 2022 The reliability of the thyristor is directly related to the safe operation of the DC transmission system. A method for evaluating the state of thyristors based on kernel principal component analysis (KPCA) is proposed, which firstly considers the ...
Zhaoyu Lei +6 more
doaj +1 more source
Kernal principal component analysis of the ear morphology [PDF]
, 2017 This paper describes features in the ear shape that change across a population of ears and explores the corresponding changes in ear acoustics. The statistical analysis conducted over the space of ear shapes uses a kernel principal component analysis ...
Epain, Nicolas +4 more
core +3 more sources
Training Echo State Networks with Regularization through Dimensionality Reduction [PDF]
, 2016 In this paper we introduce a new framework to train an Echo State Network to predict real valued time-series. The method consists in projecting the output of the internal layer of the network on a space with lower dimensionality, before training the ...
Bianchi, Filippo Maria +2 more
core +2 more sources
Submarine Threat Degree Assessment Model Based on Hybrid Kernel Principal Component Analysis [PDF]
Jisuanji gongcheng, 2018 Target threat degree assessment is a crucial link in submarine operations.In order to reduce the complexity of the evaluation and improve the accuracy of evaluation,according to the diversity of the target space sources,the submarine threat degree ...
DONG Xue,ZHANG Deping
doaj +1 more source
Efficient Clustering on Riemannian Manifolds: A Kernelised Random Projection Approach [PDF]
, 2015 Reformulating computer vision problems over Riemannian manifolds has demonstrated superior performance in various computer vision applications. This is because visual data often forms a special structure lying on a lower dimensional space embedded in a ...
Alavi, Azadeh +3 more
core +2 more sources
Mine water inrush source identification model based on KPCA-GWO-SVM
Meikuang Anquan, 2023 In order to improve the accuracy of mine water inrush source identification, a KPCA-GWO-SVM-based mine water inrush source identification model is proposed.
HUA Xingyue, SHAO Liangshan
doaj +1 more source
Robust Kernel Principal Component Analysis With ℓ2,1-Regularized Loss Minimization
IEEE Access, 2020 Principal component analysis (PCA) is a widely used unsupervised method for dimensionality reduction. The kernelized version is called kernel principal component analysis (KPCA), which can capture the nonlinear data structure.
Duo Wang, Toshihisa Tanaka
doaj +1 more source
Optimized kernel minimum noise fraction transformation for hyperspectral image classification [PDF]
, 2017 This paper presents an optimized kernel minimum noise fraction transformation (OKMNF) for feature extraction of hyperspectral imagery. The proposed approach is based on the kernel minimum noise fraction (KMNF) transformation, which is a nonlinear ...
Gao, Lianru +4 more
core +2 more sources