Problem statement: In facial biometrics, face features are used as the required human traits for automatic recognition. Feature extracted from face images are significant for face biometrics system performance. Approach: In this thesis, a framework of facial biometric was designed based on two subspace methods i.e., Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Firs...

Null linear discriminant analysis (LDA) method is a popular dimensionality reduction method for solving small sample size problem. The implementation of null LDA method is, however, computationally very expensive. In this paper, we theoretically derive the null LDA method from a different perspective and present a computationally efficient implementation of this method. Eigenvalue decomposition...

In this paper, we describe our experiments using Latent Dirichlet Allocation (LDA) to model images containing both perceptual features and words. To build a large-scale image tagging system, we distribute the computation of LDA parameters using MapReduce. Empirical study shows that our scalable LDA supports image annotation both effectively and efficiently.

We present a novel method for dimensionality reduction and recognition based on Linear Discriminant Analysis (LDA), which specifically deals with the Small Sample Size (SSS) problem in Computer Vision applications. Unlike the traditional methods, which impose specific assumptions to address the SSS problem, our approach introduces a variant of bootstrap bumping technique, which is a general fra...

Nuisance attribute projection (NAP) and within-class covariance normalization (WCCN) are two effective techniques for intersession variability compensation in SVM based speaker verification systems. However, by normalizing or removing the nuisance subspace containing the session variability can not guarantee to enlarge the distance between speakers. In this paper, we investigated the probabilit...

Formant trajectories have been shown to convey a great deal of speaker-specific information and their speaker-discriminatory potential has been quantified using Linear Discriminant Analysis on laboratory material [16]. This study tests the applicability of LDA on three sets of real-case forensic recordings. Given the limitations of LDA, we used the actual formant trajectory values (F1–F3) and c...

The objective of this work was to evaluate hematological, biochemical and ruminant parameters for diagnosis and treatment of the left displaced abomasum (LDA) in dairy cows, in the Plateau Region of Rio Grande do Sul, Brazil. Ruminant fluid, blood and urine samples were collected from 20 cows suffering LDA and from 20 healthy cows (control). The cows with LDA showed lower values of daily milk p...

Computation of selectional preferences, the admissible argument values for a relation, is a well studied NLP task with wide applicability. We present LDA-SP, the first LDA-based approach to computing selectional preferences. By simultaneously inferring latent topics and topic distributions over relations, LDA-SP combines the benefits of previous approaches: it is competitive with the non-class-...

Experiments and computer simulations of the transformations of amorphous ices display different behaviors depending on sample preparation methods and on the rates of change of temperature and pressure to which samples are subjected. In addition to these factors, simulation results also depend strongly on the chosen water model. Using computer simulations of the ST2 water model, we study how the...

When it becomes necessary to reduce the complexity of a classifier, dimensionality reduction can be an effective way to address classifier complexity. Linear Discriminant Analysis (LDA) is one approach to dimensionality reduction that makes use of a linear transformation matrix. The widely used Fisher’s LDA is “sub-optimal” when the sample class covariance matrices are unequal, meaning that ano...