Identification of temporal variations in mental workload using locally-linear-embedding-based EEG feature reduction and support-vector-machine-based clustering and classification techniques

2014 ◽  
Vol 115 (3) ◽  
pp. 119-134 ◽  
Author(s):  
Zhong Yin ◽  
Jianhua Zhang
Keyword(s):  
Support Vector Machine ◽  
Mental Workload ◽  
Temporal Variations ◽  
Feature Reduction ◽  
Locally Linear Embedding ◽  
Support Vector ◽  
Classification Techniques ◽  
Clustering And Classification ◽  
Linear Embedding ◽  
Locally Linear

Machinery fault diagnosis via an improved multi-linear subspace and locally linear embedding

2018 ◽  
Vol 40 (14) ◽  
pp. 4014-4026
Author(s):  
Yansheng Zhang ◽  
Dong Ye ◽  
Yuanhong Liu ◽  
Yu Cai
Keyword(s):  
Fault Diagnosis ◽  
Linear Subspace ◽  
Vibration Signal ◽  
Vector Model ◽  
Locally Linear Embedding ◽  
Support Vector ◽  
Multiple Sensors ◽  
Diagnosis Method ◽  
Linear Embedding ◽  
Locally Linear

Traditional fault diagnosis methods mainly depend on the vector model to describe a signal, which will lead to information loss and the curse of dimensionality. In order to overcome these problems, in this paper an improved multi-linear subspace (MLS) method and locally linear embedding (LLE) are integrated (MLSLLE) to extract significant features. To obtain more information, first it is suggested that multiple sensors should be used to sample the vibration signal of a machine from different positions; then, these data are projected into different subspaces, where each sample is represented as a tensor form, respectively; finally, higher-order singular value decomposition and LLE are introduced to extract significant features. Thus a fault diagnosis method is proposed based on MLSLLE and support vector machines. The advantages of the proposed fault diagnosis method are validated by two real bearing data sets.

Application of Locally Linear Embedding Based on Improved Distance in Neuron Classification

2014 ◽  
Vol 926-930 ◽  
pp. 2996-2999
Author(s):  
Zhen Zhen Wang ◽  
Xiao Jun Tong ◽  
Shan Zeng
Keyword(s):  
Euclidean Distance ◽  
Locally Linear Embedding ◽  
Support Vector ◽  
Morphological Parameters ◽  
Svm Algorithm ◽  
Good Classification ◽  
Linear Embedding ◽  
Sample Component ◽  
Locally Linear

For locally linear embedding (LLE) algorithm of the shortcoming, an improved distance algorithm LLE is proposed, in locally linear embedding algorithm the distribution of sample component is different and the Euclidean distance can’t reflect sample distance actually. In the experiment, a sample of 231 neurons is obtained, and the morphological parameters of neurons are calculated firstly. Second, the improved locally linear embedding algorithm is used to reduce data dimensionality. Finally, support vector machine (SVM) algorithm is used to train and test samples. Experimental results show under certain conditions the classification of the method has good classification.

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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
Sign in / Sign up

Export Citation Format

Share Document