scholarly journals Neural Network Implementations for PCA and Its Extensions

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Jialin Qiu ◽  
Hui Wang ◽  
Jiabin Lu ◽  
Biaobiao Zhang ◽  
K.-L. Du
Keyword(s):  
Neural Network ◽  
Minor Component ◽  
Principal Component ◽  
Component Analysis ◽  
Adaptive Signal Processing ◽  
Singular Value ◽  
Eigenvalue Decomposition ◽  
Blind Signal Separation ◽  
Neural Network Models ◽  
Linear Discriminant

Many information processing problems can be transformed into some form of eigenvalue or singular value problems. Eigenvalue decomposition (EVD) and singular value decomposition (SVD) are usually used for solving these problems. In this paper, we give an introduction to various neural network implementations and algorithms for principal component analysis (PCA) and its various extensions. PCA is a statistical method that is directly related to EVD and SVD. Minor component analysis (MCA) is a variant of PCA, which is useful for solving total least squares (TLSs) problems. The algorithms are typical unsupervised learning methods. Some other neural network models for feature extraction, such as localized methods, complex-domain methods, generalized EVD, and SVD, are also described. Topics associated with PCA, such as independent component analysis (ICA) and linear discriminant analysis (LDA), are mentioned in passing in the conclusion. These methods are useful in adaptive signal processing, blind signal separation (BSS), pattern recognition, and information compression.

Bankruptcy Prediction Using Principal Component Analysis Based Neural Network Models

2010 ◽  
pp. 138-156
Author(s):  
G. A. Rekha Pai ◽  
G. A. Vijayalakshmi Pai
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Prediction Models ◽  
Network Models ◽  
Principal Component ◽  
Component Analysis ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Neural Network Models ◽  
The Individual

Industrial bankruptcy is a rampant problem which does not occur overnight and when it occurs can cause acute financial embarrassment to Governments and financial institutions as well as threaten the very viability of the firms. It is therefore essential to help industries identify the impending trouble early. Several statistical and soft computing based bankruptcy prediction models that make use of financial ratios as indicators have been proposed. Majority of these models make use of a selective set of financial ratios chosen according to some appropriate criteria framed by the individual investigators. In contrast, this study considers any number of financial ratios irrespective of the industrial category and size and makes use of Principal Component Analysis to extract their principal components, to be used as predictors, thereby dispensing with the cumbersome selection procedures used by its predecessors. An Evolutionary Neural Network (ENN) and a Backpropagation Neural Network with Levenberg Marquardt’s training rule (BPN) have been employed as classifiers and their performance has been compared using Receiver Operating Characteristics (ROC) analyses. Termed PCA-ENN and PCA-BPN models, the predictive potential of the two models have been analyzed over a financial database (1997-2000) pertaining to 34 sick and 38 non sick Indian manufacturing companies, with 21 financial ratios as predictor variables.

scholarly journals Internal Crack Prediction of Continuous Casting Billet Based on Principal Component Analysis and Deep Neural Network

Metals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1976
Author(s):  
Leilei Zou ◽  
Jiangshan Zhang ◽  
Yanshen Han ◽  
Fanzheng Zeng ◽  
Quanhui Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Continuous Casting ◽  
Deep Neural Network ◽  
Principal Component ◽  
Component Analysis ◽  
Learning Ability ◽  
Internal Crack ◽  
Strong Nonlinearity ◽  
Neural Network Models

The accurate prediction of internal cracks in steel billets is of great importance for the stable production of continuous casting. However, it is challenging, owing to the strong nonlinearity, and coupling among continuous casting process parameters. In this study, an internal crack prediction model based on the principal component analysis (PCA) and deep neural network (DNN) was proposed by collecting sufficient industrial data. PCA was used to reduce the dimensionality of the factors influencing the internal cracks, and the obtained principal components were used as DNN input variables. The 5-fold cross-validation results demonstrate that the prediction accuracy of the DNN model is 92.2%, which is higher than those of the decision tree (DT), extreme learning machine (ELM), and backpropagation (BP) neural network models. Moreover, the variance analysis showed that the prediction results of the DNN model were more stable. The PCA-DNN model can provide a useful reference for real production, owing to its strong learning ability and fault-tolerant ability.

BEASF-Based Image Enhancement for Caries Detection Using Multidimensional Projection and Neural Network

2018 ◽  
Vol 8 (2) ◽  
pp. 47-66
Author(s):  
Shashikant Patil ◽  
Vaishali Kulkarni ◽  
Archana Bhise
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
False Negative ◽  
Research Work ◽  
False Negative Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Oral Disease ◽  
Caries Detection ◽  
Linear Discriminant

Tooth caries or cavities diagnosing are concerned as the most significant research work, as this is the common oral disease suffered by humans. Many approaches have been proposed under the topics including demineralization and decaying as well. However, the imaging modalities often suffer from various critical or complex aspects that struggles the methods to attain accurate diagnosis. This article turns to introduce a new cavity diagnosis model with three phases: (i) pre-processing (ii) feature extraction (iii) classification. In the first phase, a new bi-histogram equalization with adaptive sigmoid functions (BEASF) is introduced to enhance the image quality followed by other enhancements models like grey thresholding and active contour. Then, the features are extracted using multilinear principal component analysis (MPCA). Further, the classification is done via neural network (NN) classifier. After the implementation, the proposed model compares its performance over other conventional methods like principal component analysis (PCA), linear discriminant analysis (LDA) and independent component analysis (ICA) and the performance of the approach is analyzed in terms of measures such as accuracy, sensitivity, specificity, precision, false positive rate (FPR), false negative rate (FNR), negative predictive value (NPV), false discovery rate (FDR), F1Score and Mathews correlation coefficient (MCC), and proves the superiority of proposed work.

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