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...

The slip correction factor has been investigated at reduced pressures and high Knudsen number using polystyrene latex (PSL) particles. Nano-differential mobility analyzers (NDMA) were used in determining the slip correction factor by measuring the electrical mobility of 100.7 nm, 269 nm, and 19.90 nm particles as a function of pressure. The aerosol was generated via electrospray to avoid multip...

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 ...

ARP Angle Resolved Photoemission DFT Density Functional Theory FT Frustrated Translation HATOF He Atoms Time of Flight HREELS High Resolution Electron Energy Loss Spectroscopy IRAS Infrared Reflection Absorption Spectroscopy L Longitudinally polarised mode LDA Local Density Approximation LDM Lattice Dynamical Model LEED Low Energy Electron Diffraction ML Monolayer NEXAFS Near Edge X-rays Absorp...

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...

ZnO columnar single crystals were formed by pulsed laser ablation in deionized water and surfactant aqueous solutions of lauryl dimethylaminoacetic acid (LDA) and cetyltrimethylammonium bromide (CTAB) at 80 degrees C. ZnO particles produced by laser ablation were dissolved at a higher temperature than 60 degrees C, and then crystalline growth to columnar structure proceeded. While large ZnO col...