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Machine Learning and Dimension Reduction methods for High‐Dimensional Datasets

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  • Full or part time
    Dr A Artemiou
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

In today’s environment where computer processors are powerful and computer memory cheap, researchers are able to collect and store huge amounts of data. Analysing that data needs sophisticated statistical and computational methods as most classic statistical methodology was developed at an era where data collection was not as easy and datasets where a lot of orders of magnitude smaller. Sufficient dimension reduction (SDR) is a class of methods for feature extraction in regression and classification problems with the purpose of reducing the size of a multidimensional dataset to a few important features.

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

Funding Notes

The studentship is funded through the EPSRC Doctoral Training Partnership and Cardiff School of Mathematics. It consists of full UK/EU tuition fees, as well as a Doctoral Stipend matching UK Research Council National Minimum.
UK Research Council eligibility conditions apply
Additional funding is available over the course of the programme and will cover costs such as research consumables, training, conferences and travel.

References

Applicants should submit an application for postgraduate study via the Cardiff University Online Application Service.
http://www.cardiff.ac.uk/study/postgraduate/applying/how-to-apply/online-application-service/mathematics-research
Applicants should select Doctor of Philosophy (Mathematics), with a start date of October 2018.

In the research proposal section of your application, please specify the project title and supervisors of this project and copy the project description in the text box provided. In the funding section, please select "I will be applying for a scholarship / grant" and specify that you are applying for advertised funding from EPSRC DTP.

If applying for more than one Cardiff University project please note this in the research proposal section.

Shortlisted candidates will be invited to attend an interview after the closing date

How good is research at Cardiff University in Mathematical Sciences?

FTE Category A staff submitted: 24.05

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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