Feature extraction based on supervised kernel locality preserving projection algorithm and application to fault diagnosis

2009 ◽  
Author(s):  
DeCheng Wang ◽  
Hui Lin
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Projection Algorithm ◽  
Locality Preserving Projection ◽  
Locality Preserving

Face Recognition Based on Rearranged Modular Two-Dimensional Locality Preserving Projection

2018 ◽  
Vol 32 (12) ◽  
pp. 1856016 ◽  
Author(s):  
Huxidan Jumahong ◽  
Gulnaz Alimjan
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Projection Algorithm ◽  
Two Dimensional ◽  
Spatial Structures ◽  
Locality Preserving Projection ◽  
Face Images ◽  
Locality Preserving ◽  
Local Block ◽  
Novel Algorithm

This paper proposes a novel algorithm for feature extraction for face recognition, namely the rearranged modular two-dimensional locality preserving projection (Rm2DLPP). In the proposed algorithm, the original images are first divided into modular blocks, then the subblocks are rearranged to form two-dimensional matrices and finally the two-dimensional locality preserving projection algorithm is applied directly on the arranged matrices. The advantage of the Rm2DLPP algorithm is that it can utilize the local block features and global spatial structures of 2D face images simultaneously. The performance of the proposed method is evaluated and compared with other face recognition methods on the ORL, AR and FERET databases. The experimental results demonstrate the effectiveness and superiority of the proposed approach.

scholarly journals An Improved Metric Learning Approach for Degraded Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Guofeng Zou ◽  
Yuanyuan Zhang ◽  
Kejun Wang ◽  
Shuming Jiang ◽  
Huisong Wan ◽  
...  
Keyword(s):  
Face Recognition ◽  
Metric Learning ◽  
Computing Time ◽  
Feature Space ◽  
Projection Algorithm ◽  
Learning Approach ◽  
Learning Method ◽  
Matching Problem ◽  
Locality Preserving Projection ◽  
Locality Preserving

To solve the matching problem of the elements in different data collections, an improved coupled metric learning approach is proposed. First, we improved the supervised locality preserving projection algorithm and added the within-class and between-class information of the improved algorithm to coupled metric learning, so a novel coupled metric learning method is proposed. Furthermore, we extended this algorithm to nonlinear space, and the kernel coupled metric learning method based on supervised locality preserving projection is proposed. In kernel coupled metric learning approach, two elements of different collections are mapped to the unified high dimensional feature space by kernel function, and then generalized metric learning is performed in this space. Experiments based on Yale and CAS-PEAL-R1 face databases demonstrate that the proposed kernel coupled approach performs better in low-resolution and fuzzy face recognition and can reduce the computing time; it is an effective metric method.

Feature Extraction of Hyperspectral Images of Weighted Spatial and Spectral Locality Preserving Projection (WSSLPP)

2017 ◽  
Vol 25 (1) ◽  
pp. 263-273 ◽  
Author(s):  
何 芳 HE Fang ◽  
王 榕 WANG Rong ◽  
于 强 YU Qiang ◽  
贾维敏 JIA Wei-min
Keyword(s):  
Feature Extraction ◽  
Hyperspectral Images ◽  
Locality Preserving Projection ◽  
Locality Preserving

Two-Dimensional Locality Discriminant Preserving Projections for Face Recognition

2010 ◽  
Vol 121-122 ◽  
pp. 391-398 ◽  
Author(s):  
Qi Rong Zhang ◽  
Zhong Shi He
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Image Feature ◽  
Two Dimensional ◽  
Face Database ◽  
Image Feature Extraction ◽  
Locality Preserving Projection ◽  
Locality Preserving

In this paper, we propose a new face recognition approach for image feature extraction named two-dimensional locality discriminant preserving projections (2DLDPP). Two-dimensional locality preserving projections (2DLPP) can direct on 2D image matrixes. So, it can make better recognition rate than locality preserving projection. We investigate its more. The 2DLDPP is to use modified maximizing margin criterion (MMMC) in 2DLPP and set the parameter optimized to maximize the between-class distance while minimize the within-class distance. Extensive experiments are performed on ORL face database and FERET face database. The 2DLDPP method achieves better face recognition performance than PCA, 2DPCA, LPP and 2DLPP.

scholarly journals Multisensor Fused Fault Diagnosis for Rotation Machinery Based on Supervised Second-Order Tensor Locality Preserving Projection and Weighted k-Nearest Neighbor Classifier under Assembled Matrix Distance Metric

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Fen Wei ◽  
Gang Wang ◽  
Bingyin Ren ◽  
Jianghua Ge ◽  
Yaping Wang
Keyword(s):  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Second Order ◽  
Distance Metric ◽  
Order Tensor ◽  
Locality Preserving Projection ◽  
Nearest Neighbor Classifier ◽  
Locality Preserving ◽  
Diagnosis Method ◽  
Neighbor Classifier

In order to sufficiently capture the useful fault-related information available in the multiple vibration sensors used in rotation machinery, while concurrently avoiding the introduction of the limitation of dimensionality, a new fault diagnosis method for rotation machinery based on supervised second-order tensor locality preserving projection (SSTLPP) and weighted k-nearest neighbor classifier (WKNNC) with an assembled matrix distance metric (AMDM) is presented. Second-order tensor representation of multisensor fused conditional features is employed to replace the prevailing vector description of features from a single sensor. Then, an SSTLPP algorithm under AMDM (SSTLPP-AMDM) is presented to realize dimensional reduction of original high-dimensional feature tensor. Compared with classical second-order tensor locality preserving projection (STLPP), the SSTLPP-AMDM algorithm not only considers both local neighbor information and class label information but also replaces the existing Frobenius distance measure with AMDM for construction of the similarity weighting matrix. Finally, the obtained low-dimensional feature tensor is input into WKNNC with AMDM to implement the fault diagnosis of the rotation machinery. A fault diagnosis experiment is performed for a gearbox which demonstrates that the second-order tensor formed multisensor fused fault data has good results for multisensor fusion fault diagnosis and the formulated fault diagnosis method can effectively improve diagnostic accuracy.

scholarly journals Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm

2018 ◽  
Vol 63 (3) ◽  
pp. 035020 ◽  
Author(s):  
Morteza Heidari ◽  
Abolfazl Zargari Khuzani ◽  
Alan B Hollingsworth ◽  
Gopichandh Danala ◽  
Seyedehnafiseh Mirniaharikandehei ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Projection Algorithm ◽  
Learning Approach ◽  
Locality Preserving Projection ◽  
Locality Preserving ◽  
Machine Learning Approach
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