Linear discriminant analysis (LDA) is a popular dimensionality reduction and classification method that simultaneously maximizes between-class scatter and minimizes within-class scatter. In this paper, we verify the equivalence of LDA and least squares (LS) with a set of dependent variable matrices. The equivalence is in the sense that the LDA solution matrix and the LS solution matrix have the...

Linear Discriminant Analysis (LDA) followed by a diagonalizing maximum likelihood linear transform (MLLT) applied to spliced static MFCC features yields important performance gains as compared to MFCC+dynamic features in most speech recognition tasks. It is reasonable to regularize LDA transform computation for stability. In this paper, we regularize LDA and heteroschedastic LDA transforms usin...

Structural and mechanistic studies of the lithium diisopropylamide (LDA)-mediated anionic Fries rearrangements of aryl carbamates are described. Substituents at the meta position of the arene (H, OMe, F) and the dialkylamino moiety of the carbamate (Me(2)N, Et(2)N, and i-Pr(2)N) markedly influence the relative rates of ortholithiation and subsequent Fries rearrangement. Structural studies using...

In this paper, a novel face recognition method based on Gabor-wavelet and linear discriminant analysis (LDA) is proposed. Given training face images, discriminant vectors are computed using LDA. The function of the discriminant vectors is two-fold. First, discriminant vectors are used as a transform matrix, and LDA features are extracted by projecting original intensity images onto discriminant...

We propose a novel approach to boosting weighted linear discriminant analysis (LDA) as a weak classifier. Combining Adaboost with LDA allows selecting the most relevant features for classification at each boosting iteration, thus benefiting from feature correlation. The advantages of this approach include the use of a smaller number of weak learners to achieve a low error rate, improved classif...

Latent Dirichlet Allocation (LDA) is a three-level hierarchical Bayesian model for topic inference. In spite of its great success, inferring the latent topic distribution with LDA is time-consuming. Motivated by the transfer learning approach proposed by Hinton et al. (2015), we present a novel method that uses LDA to supervise the training of a deep neural network (DNN), so that the DNN can ap...

Background The hypothesis that the LDA (low-dose-aspirin) could improve ovarian and uterine perfusion induced clinicians to administer it in women undergoing IVF. Unfortunately, no studies have shown significant differences in terms of pregnancy rate among patients treated or untreated with LDA. The absence of proven clinical benefits of LDA supplementation, led us to wonder whether aspirin, ad...

In machine learning, linear discriminant analysis (LDA) is a popular dimension reduction method. In this paper, we first provide a new perspective of LDA from an information theory perspective. From this new perspective, we propose a new formulation of LDA, which uses the pairwise averaged class covariance instead of the globally averaged class covariance used in standard LDA. This pairwise (av...

BACKGROUND Overt bleeding associated with low dose aspirin (LDA) is well-recognized, little attention is given to the possibility of association between LDA and occult bleeding, although this is known to occur in healthy volunteers. LDA is used increasingly in primary and secondary prevention of a number of medical conditions, many of which are common in older people, as is anemia. Anemia in ol...