A Polygonal Line Algorithm based Nonlinear Feature Extraction Method

Linear Embedding ◽

This paper deals with the study of Locally Linear Embedding (LLE) and Hessian LLE nonlinear feature extraction for high dimensional data dimension reduction. LLE and Hessian LLE algorithm which reveals the characteristics of nonlinear manifold learning were analyzed. LLE and Hessian LLE algorithm simulation research was studied through different kinds of sample for dimensionality reduction. LLE and Hessian LLE algorithm’s classification performance was compared in accordance with MDS. The simulation experimental results show that LLE and Hessian LLE are very effective feature extraction method for nonlinear manifold learning.

2009 Asia-Pacific Conference on Computational Intelligence and Industrial Applications (PACIIA) ◽

A fast KPCA-based nonlinear feature extraction method

10.1109/paciia.2009.5406645 ◽

2009 ◽

Author(s):

Jinghua Wang ◽

Binglei Xie ◽

Jiajie Xu ◽

Haifen Chen

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Exploring supervised neighborhood preserving embedding (SNPE) as a nonlinear feature extraction method for vibrational spectroscopic discrimination of agricultural samples according to geographical origins

Talanta ◽

10.1016/j.talanta.2015.07.028 ◽

2015 ◽

Vol 144 ◽

pp. 960-968 ◽

Cited By ~ 6

Author(s):

Sanguk Lee ◽

Jinyoung Hwang ◽

Hyeseon Lee ◽

Hoeil Chung

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Nonlinear feature extraction method of pitch control signal in Erhu works based on graph convolutional neural network

10.1109/phm-nanjing52125.2021.9613067 ◽

2021 ◽

Author(s):

Xiaofeng Du

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Convolutional Neural Network ◽

Extraction Method ◽

Control Signal ◽

Pitch Control ◽

Kernel Orthogonal Neighborhood Preserving Discriminant Analysis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.339.571 ◽

2011 ◽

Vol 339 ◽

pp. 571-574

Author(s):

Xing Zhu Liang ◽

Jing Zhao Li ◽

Yu E Lin

Keyword(s):

Feature Extraction ◽

Discriminant Analysis ◽

Extraction Method ◽

Learning Algorithms ◽

Recognition Rate ◽

Kernel Matrix ◽

Nonlinear Features ◽

Several orthogonal feature extraction algorithms based on local preserving projection have recently been proposed. However, these methods still are linear techniques in nature. In this paper, we present nonlinear feature extraction method called Kernel Orthogonal Neighborhood Preserving Discriminant Analysis (KONPDA). A major advantage of the proposed method is that it is regarded every column of the kernel matrix as a corresponding sample. Then running KONPDA in kernel matrix, nonlinear features can be extracted. Experimental results on ORL database indicate that the proposed KONPDA method achieves higher recognition rate than the ONPDA method and other kernel-based learning algorithms.

NONLINEAR FEATURE EXTRACTION AND DIMENSION REDUCTION BY POLYGONAL PRINCIPAL CURVES

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001406004508 ◽

2006 ◽

Vol 20 (01) ◽

pp. 63-78 ◽

Cited By ~ 5

Author(s):

FENG ZHANG

Keyword(s):

Feature Extraction ◽

Principal Component ◽

Polygonal Line ◽

Principal Curves ◽

Redundancy Elimination ◽

Principal Curve ◽

Nonlinear Feature ◽

Nonlinear Pattern

In this article we propose a polygonal principal curve based nonlinear feature extraction method, which achieves statistical redundancy elimination without loss of information and provides more robust nonlinear pattern identification for high-dimensional data. Recognizing the limitations of linear statistical methods, this article integrates local principal component analysis (PCA) with a polygonal line algorithm to approximate the complicated nonlinear data structure. Experimental results demonstrate that the proposed algorithm can be implemented to reduce the computation complexity for nonlinear feature extraction in multivariate cases.

A new nonlinear feature extraction method for face recognition

Neurocomputing ◽

10.1016/j.neucom.2005.07.005 ◽

2006 ◽

Vol 69 (7-9) ◽

pp. 949-953 ◽

Cited By ~ 16

Author(s):

Yanwei Pang ◽

Zhengkai Liu ◽

Nenghai Yu

Keyword(s):

Feature Extraction ◽

Face Recognition ◽

Extraction Method ◽

Unsupervised Nonlinear Feature Extraction Method And Its Effects On Target Detection In High-Dimensional Data

International Journal of Electrical Electronics and Data Communication ◽

10.18479/ijeedc/2015/v3i8/48353 ◽

2015 ◽

Vol 3 (8) ◽

Cited By ~ 2

Author(s):

Hamidullah Binol

Keyword(s):

Feature Extraction ◽

Target Detection ◽

Extraction Method ◽

High Dimensional Data ◽

High Dimensional ◽

A nonlinear feature extraction method for phoneme recognition

MELECON 2008 - The 14th IEEE Mediterranean Electrotechnical Conference ◽

10.1109/melcon.2008.4618535 ◽

2008 ◽

Author(s):

O. Gauci ◽

C. J. Debono ◽

P. Micallef

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Phoneme Recognition ◽

NONLINEAR FEATURE EXTRACTION USING FISHER CRITERION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001408006715 ◽

2008 ◽

Vol 22 (06) ◽

pp. 1089-1119 ◽

Cited By ~ 3

Author(s):

MATÍAS A. BUSTOS ◽

MANUEL A. DUARTE-MERMOUD ◽

NICOLÁS H. BELTRÁN

Keyword(s):

Feature Extraction ◽

Closed Form Solution ◽

Numerical Algorithms ◽

Form Solution ◽

Distribution Functions ◽

Fisher Criterion ◽

Standard Pattern ◽

In this paper the problem of nonlinear feature extraction based on the optimization of the Fisher criterion is analyzed. A new nonlinear feature extraction method is proposed. The method does not make use of numerical algorithms and it has an analytical (closed-form) solution. Moreover, no assumptions on the class probability distribution functions are imposed. The proposed method is applied to some standard pattern recognition problems and compared with other classical methodologies already proposed in the literature. The performance of the proposed method turned out to be superior when compared with the other methods studied.