1A1-O07 Generation of sight feature space from Locally Linear Embedding for motion acquisition of robots(Evolution and Learning for Robotics)

2011 ◽  
Vol 2011 (0) ◽  
pp. _1A1-O07_1-_1A1-O07_3
Author(s):  
Ryosuke MATSUI ◽  
Takayuki SOMEI ◽  
Yuichi KOBAYASHI
Keyword(s):  
Feature Space ◽  
Locally Linear Embedding ◽  
Linear Embedding ◽  
Locally Linear

Supervised Locally Linear Embedding for Fault Diagnosis

2010 ◽  
Vol 139-141 ◽  
pp. 2599-2602
Author(s):  
Zheng Wei Li ◽  
Ru Nie ◽  
Yao Fei Han
Keyword(s):  
Fault Diagnosis ◽  
Recognition Performance ◽  
Feature Space ◽  
Difficult Problem ◽  
Fault Classification ◽  
High Dimensional ◽  
Locally Linear Embedding ◽  
Local Linear Embedding ◽  
Linear Embedding ◽  
Locally Linear

Fault diagnosis is a kind of pattern recognition problem and how to extract diagnosis features and improve recognition performance is a difficult problem. Local Linear Embedding (LLE) is an unsupervised non-linear technique that extracts useful features from the high-dimensional data sets with preserved local topology. But the original LLE method is not taking the known class label information of input data into account. A new characteristics similarity-based supervised locally linear embedding (CSSLLE) method for fault diagnosis is proposed in this paper. The CSSLLE method attempts to extract the intrinsic manifold features from high-dimensional fault data by computing Euclidean distance based on characteristics similarity and translate complex mode space into a low-dimensional feature space in which fault classification and diagnosis are carried out easily. The experiments on benchmark data and real fault dataset demonstrate that the proposed approach obtains better performance compared to SLLE, and it is an accurate technique for fault diagnosis.

Improved Algorithm Base on Locally Linear Embedding

2014 ◽  
Vol 644-650 ◽  
pp. 2160-2163 ◽  
Author(s):  
Shi Min Liu ◽  
Yan Ni Deng ◽  
Yuan Xing Lv
Keyword(s):  
Learning Algorithm ◽  
Recognition Rate ◽  
Feature Space ◽  
Locally Linear Embedding ◽  
Dispersion Matrix ◽  
Redundant Information ◽  
Training Samples ◽  
Linear Embedding ◽  
Low Dimensional ◽  
Locally Linear

Locally linear embedding algorithm (LLE) , It makes up the shortcomings that the manifold learning algorithm can be only applied to training samples but not be extended to test samples . However, due to the presence of its Low-dimensional feature space redundant information,and its sample category information does not integrate into a low-dimensional embedding. For this shortcoming, here we introduce the two improved algorithms:the local linear maximum dispersion matrix algorithm (FSLLE) and the adaptive algorithm (ALLE), and the combinations of the above two algorithms.With this experience,combined Garbol and locally linear embedding algorithm (LLE) to compare each conclusion. The results proved to be effective elimination of redundant information among basis vectors and improve the recognition rate.

Locally Linear Embedding Based on Relative Manifold

2009 ◽  
Vol 20 (9) ◽  
pp. 2376-2386 ◽  
Author(s):  
Gui-Hua WEN ◽  
Ting-Hui LU ◽  
Li-Jun JIANG ◽  
Jun WEN
Keyword(s):  
Locally Linear Embedding ◽  
Linear Embedding ◽  
Locally Linear

Bearing feature extraction using multi-structure locally linear embedding

2021 ◽  
Vol 428 ◽  
pp. 280-290
Author(s):  
Yuanhong Liu ◽  
Zebiao Hu ◽  
Yansheng Zhang
Keyword(s):  
Feature Extraction ◽  
Locally Linear Embedding ◽  
Linear Embedding ◽  
Locally Linear

Guided Locally Linear Embedding

2011 ◽  
Vol 32 (7) ◽  
pp. 1029-1035 ◽  
Author(s):  
Babak Alipanahi ◽  
Ali Ghodsi
Keyword(s):  
Locally Linear Embedding ◽  
Linear Embedding ◽  
Locally Linear

Locally linear embedding and plantar pressure–time graph selection in heel pain classification: An observational, case-control study

2021 ◽  
Vol 128 ◽  
pp. 110784
Author(s):  
José-Víctor Alfaro-Santafé ◽  
Javier Alfaro-Santafé ◽  
Carla Lanuza-Cerzócimo ◽  
Antonio Gómez-Bernal ◽  
Aitor Pérez-Morcillo ◽  
...  
Keyword(s):  
Plantar Pressure ◽  
Case Control Study ◽  
Case Control ◽  
Heel Pain ◽  
Locally Linear Embedding ◽  
Time Graph ◽  
Pressure Time ◽  
Linear Embedding ◽  
Control Study ◽  
Locally Linear
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