Under the background of leap-forward development for the internet, e-commerce has played an important role in people's daily life, but huge data sizes have also brought problems, such as information overload which can be solved by using a recommendation system effectively. However, with the...

A novel class of dimension reduction methods is combined with a stochastic multi-factor panel regression-based state-space model in order to model the dynamics of yield curves whilst incorporating regression factors. This is achieved via Probabilistic Principal Component Analysis (PPCA) in which...

This thesis investigates profiling and differentiating customers through the use of statistical data mining techniques. The business application of our work centres on examining individuals’ seldomly studied yet critical consumption behaviour over an extensive time period within the context of...

Semiconductor devices are categorized by the temperature limits that the device manufacturers specify. The usage of high temperature components are common in certain markets, like the military, oil and natural gas exploration, and avionics control systems. However, manufacturers are reducing...

To solve the high-dimensionality issue and improve its accuracy in credit risk assessment, a high-dimensionality-trait-driven learning paradigm is proposed for feature extraction and classifier selection. The proposed paradigm consists of three main stages: categorization of high dimensional...

Sparse non-Gaussian component analysis (SNGCA) is an unsupervised method of extracting a linear structure from a high dimensional data based on estimating a low-dimensional non-Gaussian data component. In this paper we discuss a new approach to direct estimation of the projector on the target...