Decoding brain states from response patterns with multivariate pattern recognition techniques is a popular method for detecting multivoxel patterns of brain activation. These patterns are informative with respect to a subject's perceptual or cognitive states. Linear discriminant analysis (LDA) cannot be directly applied to fMRI data analysis because of the "few samples and large features" natur...

We propose the local density approximation (LDA) plus an on-site Coulomb self-interaction-like correction (SIC) potential for describing sp-hybridized bonds in semiconductors and insulators. We motivate the present LDA+USIC scheme by comparing the exact exchange (EXX) hole with the LDA exchange hole. The LDA+USIC method yields good band-gap energies Eg and dielectric constants ε(ω≈ 0) of Si, Ge...

In this paper, the performances of appearance-based statistical methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) are tested and compared for the recognition of colored face images. Three sets of experiments are conducted for relative performance evaluations. In the first set of experiments, the recognition performanc...

Linear Discriminant Analysis (LDA) is a well-known scheme for feature extraction and dimension reduction. It has been used widely in many applications involving high-dimensional data, such as face recognition and image retrieval. An intrinsic limitation of classical LDA is the so-called singularity problem, that is, it fails when all scatter matrices are singular. A well-known approach to deal ...

Standard LDA model suffers the problem that the topic assignment of each word is independent and word correlation hence is neglected. To address this problem, in this paper, we propose a model called Word Related Latent Dirichlet Allocation (WR-LDA) by incorporating word correlation into LDA topic models. This leads to new capabilities that standard LDA model does not have such as estimating in...

Linear Discriminant Analysis (LDA) is a popular feature extraction technique for face image recognition and retrieval. However, It often suffers from the small sample size problem when dealing with the high dimensional face data. Two-step LDA (PCA+LDA) [1–3] is a class of conventional approaches to address this problem. But in many cases, these LDA classifiers are overfitted to the training set...

In face recognition, LDA often encounters the so-called small sample size (SSS) problem, also known as curse of dimensionality. This problem occurs when the dimensionality of the data is quite large in comparison to the number of available training images. One of the approaches for handling this situation is the subspace LDA. It is a two-stage framework: it first uses PCA-based method for dimen...

Recently a kind of matrix-based discriminant feature extraction approach called 2DLDA have been drawn much attention by researchers. 2DLDA can avoid the singularity problem and has low computational costs and has been experimentally reported that 2DLDA outperforms traditional LDA. In this paper, we compare 2DLDA with LDA in view of the discriminant power and find that 2DLDA as a kind of special...