In face recognition literature, major approaches based on holistic templates and geometrical local features have been taken. Both approaches have certain advantages and disadvantages. In this paper, we explore a new method which integrates the above two approaches. Among many speciic systems, we select LDA (Linear Discriminant Analysis) and MPF (Matching Pursuit Filter) as the representative fr...

The Linear discriminant analysis (LDA) can be generalized into a nonlinear form ─ kernel LDA (KLDA) expediently by using the kernel functions. But KLDA is often referred to a general eigenvalue problem in singular case. To avoid this complication, this paper proposes an iterative algorithm for the two-class KLDA. The proposed KLDA is used as a nonlinear discriminant classifier, and the experime...

Feature extraction is an essential first step in speech recognition applications. In addition to static features extracted from each frame of speech data, it is beneficial to use dynamic features (called ∆ and ∆∆ coefficients) that use information from neighboring frames. Linear Discriminant Analysis (LDA) followed by a diagonalizing maximum likelihood linear transform (MLLT) applied to spliced...

LDA is a widely used machine learning technique for big data analysis. The application includes an inference algorithm that iteratively updates a model until it converges. A major challenge is the scaling issue in parallelization owing to the fact that the model size is huge and parallel workers need to communicate the model continually. We identify three important features of the model in para...

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.