Linear discriminant analysis

About: Linear discriminant analysis is a research topic. Over the lifetime, 18361 publications have been published within this topic receiving 603195 citations. The topic is also known as: Linear discriminant analysis & LDA.

Financial Applications of Discriminant Analysis: A Clarification

Abstract: In a recent article in this Journal Joy and Tollefson [10] (hereafter J&T) critically analyzed discriminant analysis and its application to bankruptcy analysis. The authors make several interesting points and provide a useful discussion of the application of this statistical technique in finance. There are, however, three aspects of their presentation which need further elaboration. These relate to their discussions of (1) the difference between the stability of the discriminant model and its predictive ability, (2) the alternative methods of making inferences about the relative discriminatory power of variables, and (3) the reference statistics to use in assessing classification efficiency. In commenting on these points we will make use of the data from the Altman [1] study as did J&T.

Support vector machines for classifying and describing credit applicants: detecting typical and critical regions

Abstract: Credit applicants are assigned to good or bad risk classes according to their record of defaulting. Each applicant is described by a high-dimensional input vector of situational characteristics and by an associated class label. A statistical model, which maps the inputs to the labels, can decide whether a new credit applicant should be accepted or rejected, by predicting the class label given the new inputs. Support vector machines (SVM) from statistical learning theory can build such models from the data, requiring extremely weak prior assumptions about the model structure. Furthermore, SVM divide a set of labelled credit applicants into subsets of ‘typical’ and ‘critical’ patterns. The correct class label of a typical pattern is usually very easy to predict, even with linear classification methods. Such patterns do not contain much information about the classification boundary. The critical patterns (the support vectors) contain the less trivial training examples. For instance, linear discriminant analysis with prior training subset selection via SVM also leads to improved generalization. Using non-linear SVM, more ‘surprising’ critical regions may be detected, but owing to the relative sparseness of the data, this potential seems to be limited in credit scoring practice.

Confidence-Based Rejection for Improved Pattern Recognition Myoelectric Control

Abstract: This study describes a novel myoelectric control scheme that is capable of motion rejection. As an extension of the commonly used linear discriminant analysis (LDA), this system generates a confidence score for each decision, providing the ability to reject those with a score below a selected threshold. The thresholds are class-specific and affect only the rejection characteristics of the associated class. Furthermore, because the rejection stage is implemented using the outputs of the LDA, the active motion classification accuracy of the proposed system is shown to outperform that of the LDA for all values of rejection threshold. The proposed scheme was compared to a baseline LDA-based pattern recognition system using a real-time Fitts' law-based target acquisition task. The use of velocity-based myoelectric control using the rejection classifier is shown to obey Fitts' law, producing linear regression fittings with high coefficients of determination (R2 > 0.943). Significantly higher (p <; 0.001) throughput, path efficiency, and completion rates were observed with the rejection-capable system for both able-bodied and amputee subjects.