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Access provided by: anon Sign Out. Class-Specific Reference Discriminant Analysis With Application in Human Behavior Analysis Abstract: In this paper, a novel nonlinear subspace learning technique for class-specific data representation is proposed. A novel data representation is obtained by applying nonlinear class-specific data projection to a discriminant feature space, where the data belonging to the class under consideration are enforced to be close to their class representation, while the data belonging to the remaining classes are enforced to be as far as possible from it.
A class is represented by an optimized class vector, enhancing class discrimination in the resulting feature space.
What is Discriminant Analysis?
An iterative optimization scheme is proposed to this end, where both the optimal nonlinear data projection and the optimal class representation are determined in each optimization step. The proposed approach is tested on three problems relating to human behavior analysis: Face recognition, facial expression recognition, and human action recognition.
Experimental results denote the effectiveness of the proposed approach, since the proposed class-specific reference discriminant analysis outperforms kernel discriminant analysis, kernel spectral regression, and class-specific kernel discriminant analysis, as well as support vector machine-based classification, in most cases. MDA works better as a credit scoring method in the banking environment two years before and after failure.
The study was done on the current financial crisis of Use of MDA helps banks to determine objectively the misclassification costs and its expected misclassification errors plus determining the provisions for bad debts. The study has proved that quantitative credit scoring models improve management decision making as compared to subjective assessment methods.
Discriminant Analysis for Marketing Research Applications
For improved credit and risk assessment, a combination of both qualitative and quantitave methods should be considered. The findings have shown that using the MDA, commercial banks could have improved their objective decision making by correctly classifying the credit worthiness of a customer, predicting firm's future performance as well as assessing their credit risk.
It has also shown that other than financial variables, inclusion of stability measures improves management decision making and objective provisioning of bad debts. The recent financial crisis emphasizes the need for developing objective credit scoring methods and instituting prudent risk assessment culture to limit the extent and potential of failure. Mvula Chijoriga, M.
Application of Discriminant Analysis: For Developing a Classification Model | SpringerLink
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You can start or join in a discussion here. Visit emeraldpublishing. Abstract Purpose — The purpose of this research is to investigate whether inclusion of risk assessment variables in the multiple discriminant analysis MDA model improved the banks ability in making correct customer classification, predict firm's performance and credit risk assessment.
Findings — The findings confirmed financial ratios as good classification and predictor variables of firm's performance. Practical implications — Use of MDA helps banks to determine objectively the misclassification costs and its expected misclassification errors plus determining the provisions for bad debts.
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