Latent topic analysis has emerged as one of the most effective methods for classifying, clustering and retrieving textual data. However, existing models such as Latent Dirichlet Allocation (LDA) were developed for static corpora of relatively large documents. In contrast, much of the textual content on the web, and especially social media, is temporally sequenced, and comes in short fragments s...

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...