About the Project
This has the potential of improving visualization of the most important relationships between the variables. This project will focus on the improvement of existing methodology for more accurate and computationally faster estimation algorithms to achieve SDR. Among the most interesting suggestions in the literature uses machine learning algorithms and more specifically Support Vector Machines (SVM). The method although powerful can be improved in different directions and therefore there are a number of directions that a student can take on this project. A few examples are: to derive new SDR methodology robust to outliers; to derive Sparse SDR methodology; to derive SDR methodology when we have missing predictors; to derive SDR methodology for functional data and many more.
Moreover there are many modern applications (like text data analysis) where the data are really high-dimensional and not derived from a Gaussian distribution. In those cases, the literature is rather thin in computationally effective methods for efficient dimension reduction. We are looking into developing both supervised and unsupervised dimension reduction methods (like non-Gaussian PCA, non-Gaussian CCA etc) which are computationally efficient and accurate in the results especially in the nonlinear feature extraction setting. Interested students can look into a number of directions sparse methodology, real time algorithms or applications to real datasets.
HOW TO APPLY
Applicants should submit an application for postgraduate study via the online application service: View Website
In the research proposal section of your application, please specify the project title and supervisors of this project.
Why not add a message here
Based on your current searches we recommend the following search filters.
Based on your current search criteria we thought you might be interested in these.