Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets

1997 ◽  
Vol 8 (1) ◽  
pp. 148-154 ◽  
Author(s):  
P. Demartines ◽  
J. Herault
Keyword(s):  
Neural Network ◽  
Component Analysis ◽  
Nonlinear Mapping ◽  
Data Sets ◽  
Self Organizing

Diagnosis of Breast Cancer Using Intelligent Information Systems Techniques

2016 ◽  
pp. 203-214 ◽  
Author(s):  
Ahmad Al-Khasawneh
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Cancer Diagnosis ◽  
Multilayer Perceptron ◽  
Breast Cancer Diagnosis ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Self Organizing Map ◽  
Intelligent Information ◽  
Self Organizing

Breast cancer is the second leading cause of cancer deaths in women worldwide. Early diagnosis of this illness can increase the chances of long-term survival of cancerous patients. To help in this aid, computerized breast cancer diagnosis systems are being developed. Machine learning algorithms and data mining techniques play a central role in the diagnosis. This paper describes neural network based approaches to breast cancer diagnosis. The aim of this research is to investigate and compare the performance of supervised and unsupervised neural networks in diagnosing breast cancer. A multilayer perceptron has been implemented as a supervised neural network and a self-organizing map as an unsupervised one. Both models were simulated using a variety of parameters and tested using several combinations of those parameters in independent experiments. It was concluded that the multilayer perceptron neural network outperforms Kohonen's self-organizing maps in diagnosing breast cancer even with small data sets.

Degradation assessment of ball bearings utilizing curvilinear component analysis

2018 ◽  
Vol 233 (3) ◽  
pp. 714-730 ◽  
Author(s):  
Prashant Tiwari ◽  
SH Upadhyay
Keyword(s):  
Feature Space ◽  
Component Analysis ◽  
Performance Degradation ◽  
Quantization Error ◽  
Mapping Technique ◽  
Nonlinear Mapping ◽  
Ball Bearings ◽  
Self Organizing Map ◽  
Feature Vectors ◽  
Self Organizing

The performance degradation assessment of ball bearings is of great importance to increase the efficiency and the reliability of rotating mechanical systems. The large dimensionality of feature space introduces a lot of noise and buries the potential information about faults hidden in the feature data. This paper proposes a novel health assessment method facilitated with two compatible methods, namely curvilinear component analysis and self-organizing map network. The novelty lies in the implementation of a vector quantization approach for the sub-manifolds in the feature space and to extract the fault signatures through nonlinear mapping technique. Curvilinear component analysis is a nonlinear mapping tool that can effectively represent the average manifold of the highly folded information and further preserves the local topology of the data. To answer the complications and to accomplish reliability and accuracy in bearing performance degradation assessment, the work is carried out with following steps; first, ensemble empirical mode decomposition is used to decompose the vibration signals into useful intrinsic mode functions; second, two fault features i.e. singular values and energy entropies are extracted from the envelopes of the intrinsic mode function signals; third, the extracted feature vectors under healthy conditions, further reduced with curvilinear component analysis are used to train the self-organizing map model; finally, the reduced test feature vectors are supplied to the trained self-organizing map and the confidence value is obtained. The effectiveness of the proposed technique is validated on three run-to-failure test signals with the different type of defects. The results indicate that the proposed technique detects the weak degradation earlier than the widely used indicators such as root mean square, kurtosis, self-organizing map-based minimum quantization error, and minimum quantization error-based on the principal component analysis.

Supervised Networks That Self-Organize Class Outputs

1997 ◽  
Vol 9 (3) ◽  
pp. 637-648 ◽  
Author(s):  
Ramesh R. Sarukkai
Keyword(s):  
Neural Network ◽  
Mapping Function ◽  
Real Data ◽  
Learning Problems ◽  
Data Sets ◽  
Neural Network Learning ◽  
Network Learning ◽  
Unseen Data ◽  
Class Labels ◽  
Self Organizing

Supervised, neural network, learning algorithms have proved very successful at solving a variety of learning problems; however, they suffer from a common problem of requiring explicit output labels. In this article, it is shown that pattern classification can be achieved, in a multilayered, feedforward, neural network, without requiring explicit output labels, by a process of supervised self-organization. The class projection is achieved by optimizing appropriate within-class uniformity and between-class discernibility criteria. The mapping function and the class labels are developed together iteratively using the derived self organizing backpropagation algorithm. The ability of the self-organizing network to generalize on unseen data is also experimentally evaluated on real data sets and compares favorably with the traditional labeled supervision with neural networks. In addition, interesting features emerge out of the proposed self-organizing supervision, which are absent in conventional approaches.

scholarly journals SYNTHESIS OF SELF-ORGANIZING MAP AND FEEDFORWARD NEURAL NETWORK FOR BETTER FORECASTING

2014 ◽  
pp. 68-75
Author(s):  
Oles Hodych ◽  
Yuriy Shcherbyna ◽  
Michael Zylan
Keyword(s):  
Neural Network ◽  
Feedforward Neural Network ◽  
Share Price ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Price Fluctuation ◽  
Price Data ◽  
Comparison Results ◽  
Self Organizing

In this article the authors propose an approach to forecasting the direction of the share price fluctuation, which is based on utilization of the Feedforward Neural Network in conjunction with Self-Organizing Map. It is proposed to use the Self-Organizing Map for filtration of the share price data set, whereas the Feedforward Neural Network is used to forecast the direction of the share price fluctuation based on the filtered data set. The comparison results are presented for filtered and non-filtered share price data sets.

The Influence Function of Principal Component Analysis by Self-Organizing Rule

1998 ◽  
Vol 10 (6) ◽  
pp. 1435-1444 ◽  
Author(s):  
Isao Higuchi ◽  
Shinto Eguchi
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Influence Function ◽  
Principal Component ◽  
Component Analysis ◽  
The Self ◽  
Network Approach ◽  
Neural Network Approach ◽  
Principal Component Vector ◽  
Self Organizing

This article is concerned with a neural network approach to principal component analysis (PCA). An algorithm for PCA by the self-organizing rule has been proposed and its robustness observed through the simulation study by Xu and Yuille (1995). In this article, the robustness of the algorithm against outliers is investigated by using the theory of influence function. The influence function of the principal component vector is given in an explicit form. Through this expression, the method is shown to be robust against any directions orthogonal to the principal component vector. In addition, a statistic generated by the self-organizing rule is proposed to assess the influence of data in PCA.

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