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.

Human Gait Recognition With Matrix Representation

Abstract: Human gait is an important biometric feature. It can be perceived from a great distance and has recently attracted greater attention in video-surveillance-related applications, such as closed-circuit television. We explore gait recognition based on a matrix representation in this paper. First, binary silhouettes over one gait cycle are averaged. As a result, each gait video sequence, containing a number of gait cycles, is represented by a series of gray-level averaged images. Then, a matrix-based unsupervised algorithm, namely coupled subspace analysis (CSA), is employed as a preprocessing step to remove noise and retain the most representative information. Finally, a supervised algorithm, namely discriminant analysis with tensor representation, is applied to further improve classification ability. This matrix-based scheme demonstrates a much better gait recognition performance than state-of-the-art algorithms on the standard USF HumanID Gait database

Tectonic discrimination diagrams revisited

Abstract: The decision boundaries of most tectonic discrimination diagrams are drawn by eye. Discriminant analysis is a statistically more rigorous way to determine the tectonic affinity of oceanic basalts based on their bulk-rock chemistry. This method was applied to a database of 756 oceanic basalts of known tectonic affinity ( ocean island, mid-ocean ridge, or island arc). For each of these training data, up to 45 major, minor, and trace elements were measured. Discriminant analysis assumes multivariate normality. If the same covariance structure is shared by all the classes (i.e., tectonic affinities), the decision boundaries are linear, hence the term linear discriminant analysis (LDA). In contrast with this, quadratic discriminant analysis (QDA) allows the classes to have different covariance structures. To solve the statistical problems associated with the constant-sum constraint of geochemical data, the training data must be transformed to log-ratio space before performing a discriminant analysis. The results can be mapped back to the compositional data space using the inverse log-ratio transformation. An exhaustive exploration of 14,190 possible ternary discrimination diagrams yields the Ti-Si-Sr system as the best linear discrimination diagram and the Na-Nb-Sr system as the best quadratic discrimination diagram. The best linear and quadratic discrimination diagrams using only immobile elements are Ti-V-Sc and Ti-V-Sm, respectively. As little as 5% of the training data are misclassified by these discrimination diagrams. Testing them on a second database of 182 samples that were not part of the training data yields a more reliable estimate of future performance. Although QDA misclassifies fewer training data than LDA, the opposite is generally true for the test data. Therefore LDA is a cruder but more robust classifier than QDA. Another advantage of LDA is that it provides a powerful way to reduce the dimensionality of the multivariate geochemical data in a similar way to principal component analysis. This procedure yields a small number of "discriminant functions,'' which are linear combinations of the original variables that maximize the between-class variance relative to the within-class variance.

Credit scoring using neural and evolutionary techniques

Abstract: This paper compares the predictive performance of linear discriminant analysis, neural networks, genetic algorithms and decision trees in distinguishing between good and slow payers of bank credit card accounts Predictive models were built using the evolutionary techniques and the results compared with those gained from the discriminant analysis model published in Crook et al (1992), The Service Industries Journal 12 which uses the same dataset A range of parameters under the control of the investigator was investigated We found that the predictive performance of linear discriminant analysis was superior to that of the other three techniques This is consistent with some studies but inconsistent with others

The effect of training set size and composition on artificial neural network classification

Abstract: Training set characteristics can have a significant effect on the performance of an image classification In this paper the effect of variations in training set size and composition on the accuracy of classifications of synthetic and remotely sensed data sets by an artificial neural network and discriminant analysis are assessed Attention is focused on the effects of variations in the overall size of the training set, in terms of the number of training samples, as well as on variations in the size of individual classes in the training set The results showed that higher classification accuracies were generally derived from the artificial neural network, especially when small training sets only were available It was also apparent that the opportunity of the artificial neural network to learn class appearance was influenced by the composition of the training set The results indicated that the size of each class in the training set had an effect similar to that of including a priori probabilitie