scholarly journals Tensor local linear embedding with global subspace projection optimisation

2021 ◽  
Author(s):  
Guo Niu ◽  
Zhengming Ma
Keyword(s):  
Subspace Projection ◽  
Local Linear Embedding ◽  
Local Linear ◽  
Linear Embedding

Dimensionality reduction of tensor data based on local linear embedding and mode product

2021 ◽  
pp. 1-18
Author(s):  
Ting Gao ◽  
Zhengming Ma ◽  
Wenxu Gao ◽  
Shuyu Liu
Keyword(s):  
Dimensionality Reduction ◽  
Learning Algorithm ◽  
Vector Data ◽  
Global Features ◽  
Global Mode ◽  
Local Linear Embedding ◽  
Local Linear ◽  
Clustering And Classification ◽  
Linear Embedding ◽  
Tensor Data

There are three contributions in this paper. (1) A tensor version of LLE (short for Local Linear Embedding algorithm) is deduced and presented. LLE is the most famous manifold learning algorithm. Since its proposal, various improvements to LLE have kept emerging without interruption. However, all these achievements are only suitable for vector data, not tensor data. The proposed tensor LLE can also be used a bridge for various improvements to LLE to transfer from vector data to tensor data. (2) A framework of tensor dimensionality reduction based on tensor mode product is proposed, in which the mode matrices can be determined according to specific criteria. (3) A novel dimensionality reduction algorithm for tensor data based on LLE and mode product (LLEMP-TDR) is proposed, in which LLE is used as a criterion to determine the mode matrices. Benefiting from local LLE and global mode product, the proposed LLEMP-TDR can preserve both local and global features of high-dimensional tenser data during dimensionality reduction. The experimental results on data clustering and classification tasks demonstrate that our method performs better than 5 other related algorithms published recently in top academic journals.

scholarly journals A Fast Radio Map Construction Method Merging Self-Adaptive Local Linear Embedding (LLE) and Graph-Based Label Propagation in WLAN Fingerprint Localization Systems

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 767 ◽  
Author(s):  
Yepeng Ni ◽  
Jianping Chai ◽  
Yan Wang ◽  
Weidong Fang
Keyword(s):  
Label Propagation ◽  
Positioning Accuracy ◽  
Local Linear Embedding ◽  
Map Construction ◽  
Local Linear ◽  
Linear Embedding ◽  
Fingerprint Database ◽  
Radio Map ◽  
Localization Systems ◽  
Self Adaptive

Indoor WLAN fingerprint localization systems have been widely applied due to the simplicity of implementation on various mobile devices, including smartphones. However, collecting received signal strength indication (RSSI) samples for the fingerprint database, named a radio map, is significantly labor-intensive and time-consuming. To solve the problem, this paper proposes a semi-supervised self-adaptive local linear embedding algorithm to build the radio map. First, this method uses the self-adaptive local linear embedding (SLLE) algorithm based on manifold learning to reduce the dimension of the high-dimensional RSSI samples and to extract a neighbor weight matrix. Secondly, a graph-based label propagation (GLP) algorithm is employed to build the radio map by semi-supervised learning from a large number of unlabeled RSSI samples to a few labeled RSSI samples. Finally, we propose a k self-adaptive neighbor weight (kSNW) algorithm, used for radio map construction in this paper, to realize online localization. The results of the experiments conducted in a real indoor environment show that the proposed method reduces the demand for large quantities of labeled samples and achieves good positioning accuracy. With only 25% labeled RSSI samples, our system can obtain positioning accuracy of more than 88%, within 3 m of localization errors.

Identification Method of Tool Wear Based on Locally Linear Embedding and Support Vector Machine

2012 ◽  
Vol 246-247 ◽  
pp. 1289-1293
Author(s):  
Zheng Qiang Li ◽  
Peng Nie ◽  
Shu Guo Zhao
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Acoustic Emission Signal ◽  
Support Vector ◽  
Emission Signal ◽  
Identification Method ◽  
Local Linear Embedding ◽  
Local Linear ◽  
Data Points ◽  
Linear Embedding

Aiming at the nonlinear characteristics of the tool wear Acoustic Emission signal, tool wear state identification method is proposed based on local linear embedding and vector machine supported. The local linear embedding algorithm makes high dimensional information down to low dimension feature space through commutation, and thus to compress the data for highlighting signal features. This algorithm well compensates for the weakness of linear dimension reduction failing to find datasets nonlinear structure. In this paper, acoustic emission signal is firstly made by phase space reconstruction. Using local linear embedding method, the high dimension space mapping data points are reflected into low-dimensional space corresponding data points, then extracting tool wear state characteristics, and using vector machine supported classifier to identify classification of the tool wear conditions. Experimental results show that this method is used for the exact recognition of the tool wear state, and has widespread tendency.

scholarly journals Fault Diagnosis of Supervision and Homogenization Distance Based on Local Linear Embedding Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Guangbin Wang ◽  
Jun Luo ◽  
Yilin He ◽  
Qinyi Chen
Keyword(s):  
Fault Diagnosis ◽  
Dimension Reduction ◽  
Supervised Learning ◽  
Recognition Performance ◽  
Locally Linear Embedding ◽  
Local Linear Embedding ◽  
Local Linear ◽  
Fault Recognition ◽  
Linear Embedding ◽  
Sample Points

In view of the problems of uneven distribution of reality fault samples and dimension reduction effect of locally linear embedding (LLE) algorithm which is easily affected by neighboring points, an improved local linear embedding algorithm of homogenization distance (HLLE) is developed. The method makes the overall distribution of sample points tend to be homogenization and reduces the influence of neighboring points using homogenization distance instead of the traditional Euclidean distance. It is helpful to choose effective neighboring points to construct weight matrix for dimension reduction. Because the fault recognition performance improvement of HLLE is limited and unstable, the paper further proposes a new local linear embedding algorithm of supervision and homogenization distance (SHLLE) by adding the supervised learning mechanism. On the basis of homogenization distance, supervised learning increases the category information of sample points so that the same category of sample points will be gathered and the heterogeneous category of sample points will be scattered. It effectively improves the performance of fault diagnosis and maintains stability at the same time. A comparison of the methods mentioned above was made by simulation experiment with rotor system fault diagnosis, and the results show that SHLLE algorithm has superior fault recognition performance.

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