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Modified kernel-based nonlinear feature extraction [face recognition example]

Guang Dai, Yuntao Qian, Sen Jia
2004 IEEE International Conference on Acoustics, Speech, and Signal Processing  
A group of kernel-based Fisher discrminant analysis (KFDA) algorithms has attracted much attention due to their high performance.  ...  Feature extraction techniques are widely used in many applications to pro-process data in order to reduce the complexity of subsequent processes.  ...  In this paper, we propose a modified kernel-based nonlinear feature extraction algorithm, which can break the limitations above and is very useful for the SSSP.  ... 
doi:10.1109/icassp.2004.1327212 dblp:conf/icassp/DaiQJ04 fatcat:kc5wtrlxyjfkjff5rt4vtnfx4u

Semisupervised nonlinear feature extraction for image classification

Emma Izquierdo-Verdiguier, Luis Gomez-Chova, Lorenzo Bruzzone, Gustavo Camps-Valls
2012 2012 IEEE International Geoscience and Remote Sensing Symposium  
feature extraction strategy.  ...  Feature extraction is of paramount importance for an accurate classification of remote sensing images. Techniques based on data transformations are widely used in this context.  ...  In this paper, we present a new semisupervised KPLS method for nonlinear feature extraction.  ... 
doi:10.1109/igarss.2012.6351244 dblp:conf/igarss/Izquierdo-VerdiguierGBC12 fatcat:pqlssae55zf4bmxtuc2w4wcjyq

Speaker-Independent Vowel Recognition in Persian Speech

Mohammad Nazari, Abolghasem Sayadiyan, Seyyed Majid Valiollahzadeh
2008 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications  
In this paper we discuss the applicability of the kernel-based feature extraction for speaker-independent vowels recognition, focusing on non-linear dimension reduction methods.  ...  In this short paper, we combine nonlinear kernel based mapping of data with Support Vector machine (SVM) classifier to improve efficiency of system.  ...  KERNEL-BASED FEATURE EXTRACTION 2.1.  ... 
doi:10.1109/ictta.2008.4530026 fatcat:muvy6acskrbzhltcczxtnmeona

Volterra series for analyzing MLP based phoneme posterior estimator

Joel Pinto, G.S.V.S. Sivaram, H. Hermansky, M. Magimai-Doss
2009 2009 IEEE International Conference on Acoustics, Speech and Signal Processing  
To demonstrate the applicability of Volterra series, we analyze a multilayered perceptron trained using Mel filter bank energy features and analyze its first order Volterra kernels.  ...  The identified Volterra kernels reveal the spectro-temporal patterns that are learned by the trained system for each phoneme.  ...  The Wiener kernels are estimated using cross-correlation based methods [5] . The Volterra kernels are subsequently computed from the Wiener kernels.  ... 
doi:10.1109/icassp.2009.4959958 dblp:conf/icassp/PintoSHM09 fatcat:lhicwewaivadxixogorsv636pi

A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things

Sajida Imran, Young-Bae Ko
2018 Wireless Communications and Mobile Computing  
KLFDA extracts location features in a well-preserved kernelized space. In the new kernel featured space, nonlinear RSS features are characterized effectively.  ...  We proposed a novel indoor positioning system that considers the nonlinear discriminative feature extraction of RSS using kernel local Fisher discriminant analysis (KLFDA).  ...  We performed nonlinear discriminative feature extraction of RSS using KLFDA which extracts location features in a kernel space where the nonlinear RSS features are well characterized and captured.  ... 
doi:10.1155/2018/2976751 fatcat:haqggdv73bepfllzjlz3cio7du

Nonlinear feature extraction based on centroids and kernel functions

Cheong Hee Park, Haesun Park
2004 Pattern Recognition  
The dimension reducing nonlinear transformation is obtained by implicitly mapping the input data into a feature space using a kernel function, and then finding a linear mapping based on an orthonormal  ...  A nonlinear feature extraction method is presented which can reduce the data dimension down to the number of clusters, providing dramatic savings in computational costs.  ...  An added benefit we observed in all our tests is that after the kernel-based nonlinear feature extraction by the KOC algorithm, another use of the kernel function in the SVM is not necessary.  ... 
doi:10.1016/j.patcog.2003.07.011 fatcat:5qoor4islfe6jhrcvfa3h3hroe

Cyber Attack Detection System based on Improved Support Vector Machine

Shailendra Singh, Sanjay Silakari
2015 International Journal of Security and Its Applications  
It is based on the Riemannian geometrical structure induced by the kernel function.  ...  This paper presents a novel cyber attack classification approach using improved Support Vector Machine (iSVM) by modifying Gaussian kernel.  ...  This modified kernel gives better performance compare with the original Gaussian kernel. A nonlinear SVM maps each samples of input space R into a feature space F through a nonlinear mapping .  ... 
doi:10.14257/ijsia.2015.9.9.32 fatcat:jg6jngttpjfhlgir4zlqwoxmoe

KPCA-Based Visual Fault Diagnosis for Nonlinear Industrial Process [chapter]

Jiahui Yu, Hongwei Gao, Zhaojie Ju
2019 Lecture Notes in Computer Science  
Additionally, because most of the industrial processes are non-linear, the fault diagnosis method based on Kernel Principal Component Analysis (KPCA) is used in the system design, and the implementation  ...  Here, in order to solve these problems, a visual fault diagnosis system based on LabVIEW and Matlab is designed.  ...  In the KPCA method, the correct choice of the kernel function largely determines the quality of the system's nonlinear feature extraction.  ... 
doi:10.1007/978-3-030-27541-9_13 fatcat:dg55ulqmqvbotm75kdofrztbhy

Neutral expression synthesis using kernel active shape model

Marcella Peter, Jacey-Lynn Minoi, Suriani Ab Rahman
2020 Indonesian Journal of Electrical Engineering and Computer Science  
This paper presents a modified kernel-based Active Shape Model for neutralizing and synthesizing facial expressions.  ...  In this research, a method of a modified kernel-based active shape model based on statistical-based approach is introduced to synthesize neutral (neutralize) expressions from expressional faces, with the  ...  [17] proposed the kernel PCA which has then became widely used for nonlinear feature extraction method in the face recognition system and to develop a nonlinear shape model of faces.  ... 
doi:10.11591/ijeecs.v20.i1.pp150-157 fatcat:f3zqwbnfkfcq7fyigf44um6dpu

Nonlinear Common Vectors For Pattern Classification

Hakan Cevikalp, Marian Neamtu
2005 Zenodo  
Subspace classifiers are linear methods in nature and therefore may not extract nonlinear features of classes.  ...  The kernel-based nonlinear subspace method was developed to as to overcome this limitation [7] , [8] . In this approach all data samples are mapped to a higher-dimensional feature space.  ... 
doi:10.5281/zenodo.38833 fatcat:6vewiwjwanftbpibb5werppzsi

Joint multi-domain feature learning for image steganalysis based on CNN

Ze Wang, Mingzhi Chen, Yu Yang, Min Lei, Zhexuan Dong
2020 EURASIP Journal on Image and Video Processing  
For the nonlinear detection mechanism, based on the spatial rich model (SRM), we introduce the maximum and minimum nonlinear residual feature acquisition method into the model to adapt to the nonlinear  ...  In recent years, researchers have been making great progress in the steganalysis technology based on convolution neural networks (CNN).  ...  a nonlinear feature extraction method.  ... 
doi:10.1186/s13640-020-00513-7 fatcat:y6tloszm3bfrdlxo5ty6kxl72e

Soft Sensor of Vehicle State Estimation Based on the Kernel Principal Component and Improved Neural Network

Haorui Liu, Juan Yang, Heli Yang, Fengyan Yi
2016 Journal of Sensors  
To address these problems, a vehicle state estimation method based on the kernel principal component analysis and the improved Elman neural network is proposed.  ...  and precise when tracking the vehicle states within the nonlinear area.  ...  Modeling Based on Kernel Principal Component Analysis and Modified Elman Neural Network.  ... 
doi:10.1155/2016/9568785 fatcat:cdh77wagsnfqreczstvaackuay

An Informative Feature Extraction Algorithm for Kernel Machines

J. S. Liu
2013 Elektronika ir Elektrotechnika  
Index Terms-Feature extraction, kernel machines, image processing.  ...  In this paper we propose a novel method for feature extraction tasks.  ...  [8] proposed a kernel based nonlinear feature extraction, which transforms this problem to a kernel parameter learning problem. Ref.  ... 
doi:10.5755/j01.eee.19.7.5172 fatcat:c2simx257zegjlfixmz5532jg4

Nonlinear feature extraction using kernel principal component analysis with non-negative pre-image

M Kallas, P Honeine, C Richard, H Amoud, C Francis
2010 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology  
In this paper, we propose a nonlinear feature extraction method, with a non-negativity constraint.  ...  To this end, the kernel principal component analysis is considered to define the most relevant features in the reproducing kernel Hilbert space.  ...  In this paper, we have shown that nonlinear features can be extracted by jointly applying a kernel-PCA algorithm and a pre-image technique.  ... 
doi:10.1109/iembs.2010.5627421 pmid:21096851 fatcat:25stpcqayvgvlcfdycdbh5ndyy

Modified Kernel Marginal Fisher Analysis for Feature Extraction and Its Application to Bearing Fault Diagnosis

Li Jiang, Shunsheng Guo
2016 Shock and Vibration  
This paper proposes modified kernel marginal Fisher analysis (MKMFA) for feature extraction with dimensionality reduction.  ...  A MKMFA- based fault diagnosis model is presented and applied to identify different bearing faults.  ...  Kernel Fisher discriminant analysis (KFDA) [10] and Kernel principal component analysis (KPCA) [11] are classical nonlinear feature extraction methods with dimensionality reduction.  ... 
doi:10.1155/2016/1205868 fatcat:a3ope3rbmfhi7h2uafghhtuzuu
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