Gait Recognition Based on Locality Preserving Projections

2015 ◽  
Vol 738-739 ◽  
pp. 625-630
Author(s):  
Chao Li ◽  
Jin Ye Peng ◽  
Jing Guo ◽  
Xian Feng Wang ◽  
Xu Qi Wang
Keyword(s):  
Nearest Neighbor ◽  
Wavelet Packet ◽  
Gait Recognition ◽  
Recognition Method ◽  
The Public ◽  
Nearest Neighbor Classifier ◽  
Locality Preserving Projections ◽  
Locality Preserving ◽  
Neighbor Classifier

A gait recognition method based on wavelet packet decomposition (WPD) and Locality preserving projections (LPP) is proposed in this paper. The method includes the following steps, pretreatment, feature extraction by WPD and dimensionality reduction by LPP and classification of the test samples to a corresponding class according to the nearest neighbor classifier. The experiment results on the public gait database show the effectiveness of the proposed method.

scholarly journals Machine Fault Classification Based on Local Discriminant Bases and Locality Preserving Projections

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Qingbo He ◽  
Xiaoxi Ding ◽  
Yuanyuan Pan
Keyword(s):  
Feature Extraction ◽  
Wavelet Packet ◽  
Fault Classification ◽  
Feature Extraction Method ◽  
Locality Preserving Projections ◽  
Vibration Data ◽  
Pattern Structure ◽  
Locality Preserving ◽  
Machine Fault

Machine fault classification is an important task for intelligent identification of the health patterns for a mechanical system being monitored. Effective feature extraction of vibration data is very critical to reliable classification of machine faults with different types and severities. In this paper, a new method is proposed to acquire the sensitive features through a combination of local discriminant bases (LDB) and locality preserving projections (LPP). In the method, the LDB is employed to select the optimal wavelet packet (WP) nodes that exhibit high discrimination from a redundant WP library of wavelet packet transform (WPT). Considering that the obtained discriminatory features on these selected nodes characterize the class pattern in different sensitivity, the LPP is then applied to address mining inherent class pattern feature embedded in the raw features. The proposed feature extraction method combines the merits of LDB and LPP and extracts the inherent pattern structure embedded in the discriminatory feature values of samples in different classes. Therefore, the proposed feature not only considers the discriminatory features themselves but also considers the dynamic sensitive class pattern structure. The effectiveness of the proposed feature is verified by case studies on vibration data-based classification of bearing fault types and severities.

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 On Improving the Classification of Imbalanced Data

2017 ◽  
Vol 17 (1) ◽  
pp. 45-62 ◽  
Author(s):  
Lincy Meera Mathews ◽  
Hari Seetha
Keyword(s):  
Nearest Neighbor ◽  
Imbalanced Data ◽  
Experimental Results ◽  
Minority Class ◽  
Nearest Neighbor Classifier ◽  
Neighbor Classifier

Abstract Mining of imbalanced data isachallenging task due to its complex inherent characteristics. The conventional classifiers such as the nearest neighbor severely bias towards the majority class, as minority class data are under-represented and outnumbered. This paper focuses on building an improved Nearest Neighbor Classifier foratwo class imbalanced data. Three oversampling techniques are presented, for generation of artificial instances for the minority class for balancing the distribution among the classes. Experimental results showed that the proposed methods outperformed the conventional classifier.

scholarly journals Nearest-neighbor classifier as a tool for classification of protein families

2010 ◽  
Vol 4 (9) ◽  
pp. 396-398 ◽  
Author(s):  
Mona Chaurasiya ◽  
Gohel Bakul Chandulal ◽  
Krishna Misra ◽  
Vivek Kumar Chaurasiya
Keyword(s):  
Nearest Neighbor ◽  
Protein Families ◽  
Nearest Neighbor Classifier ◽  
Neighbor Classifier
Sign in / Sign up

Export Citation Format

Share Document