Locality Preserving Projection Based Multiple Copy-Paste Forgery Detection

2020 ◽  
Author(s):  
Anjali Diwan ◽  
Anil K. Roy ◽  
Suman K. Mitra
Keyword(s):  
Multiple Copy ◽  
Forgery Detection ◽  
Locality Preserving Projection ◽  
Locality Preserving

Novel method for bearing performance degradation assessment – A kernel locality preserving projection-based approach

2013 ◽  
Vol 228 (3) ◽  
pp. 548-560 ◽  
Author(s):  
Chuang Sun ◽  
Zhousuo Zhang ◽  
Zhengjia He ◽  
Zhongjie Shen ◽  
Binqiang Chen ◽  
...  
Keyword(s):  
Test Data ◽  
Performance Degradation ◽  
Inner Product ◽  
Data Sets ◽  
Degradation Index ◽  
Locality Preserving Projection ◽  
Locality Preserving ◽  
Non Linear ◽  
Linear Information ◽  
Novel Method

Bearing performance degradation assessment is meaningful for keeping mechanical reliability and safety. For this purpose, a novel method based on kernel locality preserving projection is proposed in this article. Kernel locality preserving projection extends the traditional locality preserving projection into the non-linear form by using a kernel function and it is more appropriate to explore the non-linear information hidden in the data sets. Considering this point, the kernel locality preserving projection is used to generate a non-linear subspace from the normal bearing data. The test data are then projected onto the subspace to obtain an index for assessing bearing degradation degrees. The degradation index that is expressed in the form of inner product indicates similarity of the normal data and the test data. Validations by using monitoring data from two experiments show the effectiveness of the proposed method.

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
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