Topic
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.
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TL;DR: A novel supervised manifold learning technique called Supervised Laplacian Eigenmaps (S-LE), which makes use of class label information to guide the procedure of non-linear dimensionality reduction by adopting the large margin concept.
122 citations
TL;DR: This work develops statistical procedures for the analysis of body ratios in a consistent multivariate statistical framework and presents a statistical derivation of the allometric size vector using the method of least squares.
Abstract: The analysis of ratios of body measurements is deeply ingrained in the taxonomic literature. Whether for plants or animals, certain ratios are commonly indicated in identification keys, diagnoses, and descriptions. They often provide the only means for separation of cryptic species that mostly lack distinguishing qualitative characters. Additionally, they provide an obvious way to study differences in body proportions, as ratios reflect geometric shape differences. However, when it comes to multivariate analysis of body measurements, for instance, with linear discriminant analysis (LDA) or principal component analysis (PCA), interpretation using body ratios is difficult. Both techniques are commonly applied for separating similar taxa or for exploring the structure of variation, respectively, and require standardized raw or log-transformed variables as input. Here, we develop statistical procedures for the analysis of body ratios in a consistent multivariate statistical framework. In particular, we present algorithms adapted to LDA and PCA that allow the interpretation of numerical results in terms of body proportions. We first introduce a method called the "LDA ratio extractor," which reveals the best ratios for separation of two or more groups with the help of discriminant analysis. We also provide measures for deciding how much of the total differences between individuals or groups of individuals is due to size and how much is due to shape. The second method, a graphical tool called the "PCA ratio spectrum," aims at the interpretation of principal components in terms of body ratios. Based on a similar idea, the "allometry ratio spectrum" is developed which can be used for studying the allometric behavior of ratios. Because size can be defined in different ways, we discuss several concepts of size. Central to this discussion is Jolicoeur's multivariate generalization of the allometry equation, a concept that was derived only with a heuristic argument. Here we present a statistical derivation of the allometric size vector using the method of least squares. The application of the above methods is extensively demonstrated using published data sets from parasitic wasps and rock crabs.
122 citations
01 Sep 2009
TL;DR: The results were comparable to the measured human recognition rate with this multimodal data set and the performance was higher for LDA features compared to PCA features.
Abstract: This paper explores the recognition of expressed emotion from speech and facial gestures for the speaker-dependent case. Experiments were performed on an English audio-visual emotional database consisting of 480 utterances from 4 English male actors in 7 emotions. A total of 106 audio and 240 visual features were extracted and features were selected with Plus l-Take Away r algorithm based on Bhattacharyya distance criterion. Linear transformation methods, principal component analysis (PCA) and linear discriminant analysis (LDA), were applied to the selected features and Gaussian classifiers were used for classification. The performance was higher for LDA features compared to PCA features. The visual features performed better than the audio features and overall performance improved for the audio-visual features. In case of 7 emotion classes, an average recognition rate of 56% was achieved with the audio features, 95% with the visual features and 98% with the audio-visual features selected by Bhattacharyya distance and transformed by LDA. Grouping emotions into 4 classes, an average recognition rate of 69% was achieved with the audio features, 98% with the visual features and 98% with the audio-visual features fused at decision level. The results were comparable to the measured human recognition rate with this multimodal data set.
122 citations
TL;DR: It is concluded that the PSTH-based method is an efficient alternative to more sophisticated methods such as LDA and ANNs to study how ensemble of neurons code for discrete sensory stimuli, especially when datasets with many variables are used and when the time resolution of the neural code is one of the factors of interest.
122 citations
TL;DR: This work has shown that it has reached the perfect classification rate by using X-ray image for Covid-19 detection, and SVM classifier achieved 100.0% classification accuracy by using 10-fold cross-validation.
122 citations