Information Component Analysis via Deep Learning

Primary supervisor

Additional information

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Other projects with the same supervisor

Funding

  • Competition Funded Project (Students Worldwide)
This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.

Project description

As their prominent characteristics, perceptual data often convey the mixing information, which often results in the inadequate performance for a specific perceptual information processing task due to the interference of irrelevant information components. For example, facial images typically convey the mixing information including identity and expressions. For specific tasks like face and facial expression recognition, the mixing information components are hardly separable, which results in difficulties in either of two tasks. The same problem also exists in speech information processing where speech conveys the mixing information including linguistic, speaker, emotional and environmental characteristics. Furthermore, there is no equal amount of information for mixing components; e.g. linguistics often overwhelmingly dominates the information in speech. The nature of perceptual data gives rise to considerable challenges in their modelling, analysis and recognition.

The project is going to investigate and develop a generic approach to information component analysis for perceptual data with state-of-the-art machine learning techniques, deep learning. Surrounding the main theme on how to disentangling/extracting information components, main issues to be studied include objective-driven high level abstraction of perceptual data in flexible representation forms, novel building blocks and deep learning models including architectures and learning algorithms to carry out an information component "filter' and theoretic information aspects in measuring the extracted information components. For demonstration, an information component analysis prototype would be developed for a real application, e.g., speech or facial information component analysis. In general, this project is suitable for one who is interested in fundamental research in machine learning while it is acceptable for one who has a relevant application problem in mind and wishes to tackle their problems with an emerging technology such as deep learning.

It is worth highlighting that the hypotheses set in this project are original and hence this is an extremely challenging project of a great novelty. In order take this project, thus, it is essential to be highly self-motivated and to have excellent background knowledge in mathematics, machine learning, image or speech signal processing and good programming skills. If you are interested in this project, please first visit my research student page: http://staff.cs.manchester.ac.uk/~kechen/ for the required materials and information prior to contacting me.

Person specification

For information

Essential

Applicants will be required to evidence the following skills and qualifications.

  • You must be capable of performing at a very high level.
  • You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.

Desirable

Applicants will be required to evidence the following skills and qualifications.

  • You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.
  • You will have good time management.

General

Applicants will be required to address the following.

  • Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
  • How well does your previous study prepare you for undertaking Postgraduate Research?
  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Why do you believe you are suitable for doing Postgraduate Research?
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