I am currently a Research Fellow in the School of Computer Science, University of Birmingham. In July I will be moving to New Zealand to take up a lectureship at the University of Waikato.

I
recently completed my PhD, supervised by Ata
Kaban, where my research focused on mitigating issues associated
with working with 'Big Data' by using random projections. I submitted
my thesis on the 23rd
January
2013 and my viva took place on 29^{th}
April
2013, when my examiners were John
Shawe-Taylor and Peter
Tino.

In 2008-9 I was a Teaching Fellow in the school and admissions tutor for our MSc Natural Computation and MSc Intelligent Systems Engineering degrees. I taught modules on Machine Learning, Machine Learning (Extended), Nature Inspired Design (A) and Nature Inspired Design. My old home page is here.

I run the Measure Concentration reading group. We meet on Fridays at 13:00 in the Computer Science building – if you would like to attend please contact me for room details.

My Erdös number is 5.

Robert J. Durrant, Room 144,

School of Computer Science,

University of Birmingham,

Edgbaston,

B15 2TT

UK

e: r.j.durrant [at] cs [dot] bham [dot] ac [dot] uk

w: www.cs.bham.ac.uk/~durranrj

t: +44 (0)121 414 2884

I have a broad interest in learning and vision, both the organic and machine varieties.

I have a particular interest in computational theories of learning.

My current research is into dimensionality reduction. In particular, random projections of very high dimensional data sets into low dimensional spaces, and the effect of projection on classification performance.

I am also interested in the Measure Concentration phenomenon and its application to machine learning and statistical pattern recognition theory and practice. Along similar lines I have some (still largely unexplored) interest in randomized algorithms.

R.J. Durrant and Ata Kaban. Random Projections as Regularizers: Learning a Linear Discriminant from Fewer Observations than Dimensions. (Submitted). pdf

R.J. Durrant and Ata Kaban. A tight bound on the performance of Fisher's linear discriminant in randomly projected data spaces. Pattern Recognition Letters, 33(7), pp 911 – 919, 2012. pdf

R.J. Durrant and Ata Kaban. When Is 'Nearest Neighbor' Meaningful: A Converse Theorem and Implications. Journal of Complexity, 25(4), pp 385-397, August 2009. pdf

R.J. Durrant and A. Kaban. Sharp Generalization Error Bounds for Randomly-projected Classifiers. Proceedings 30th International Conference on Machine Learning (ICML 2013). To Appear. pdf

A. Kaban, J. Bootkrajang,
and R.J. Durrant. Towards Large Scale Continuous EDA: A Random Matrix
Theory Perspective. Proceedings 22nd International
Conference on Genetic Algorithms and 18^{th}
Annual Genetic Programming
Conference (GECCO 2013 ). (Nominated for best paper award, EDA
track). To Appear. pdf

R.J. Durrant and A. Kaban. Error bounds for Kernel Fisher Linear Discriminant in Gaussian Hilbert Space. Proceedings 15th International Conference on Artificial Intelligence and Statistics (AIStats 2012). JMLR W&CP 22: pp 337-345, 2012. pdf

R.J. Durrant and A. Kaban. Compressed Fisher Linear Discriminant Analysis: Classification of Randomly Projected Data. Proceedings 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), pp 1119-1128. pdf

R.J. Durrant and A. Kaban. A bound on the performance of LDA in randomly projected data spaces. Proceedings 20th International Conference on Pattern Recognition (ICPR 2010). (Corrected) (IBM Best Student Paper Award in the Pattern Recognition and Machine Learning track.) pdf

A. Kaban and R.J. Durrant. Learning with Lq<1 vs L1-norm regularization with exponentially many irrelevant features. Proceedings 19th European Conference on Machine Learning (ECML08), 15-19 Sept 2008, Antwerp, Belgium. W. Daelemans et al. (eds.): LNAI 5211, pp. 580-596. Springer. pdf

A. Kaban and R.J. Durrant. A norm-concentration argument for non-convex regularization. ICML/UAI/COLT Workshop on Sparse Optimization and Variable Selection, 9 July, 2008, Helsinki, Finland. Slides (pdf)

R.J. Durrant. Learning in High Dimensions with Projected Linear Discriminants. pdf

R.J. Durrant and Ata Kaban. Random Projections as Regularizers: Learning a Linear Discriminant Ensemble from Fewer Observations than Dimensions. School Technical Report CSR-12-01. pdf

R.J. Durrant and Ata Kaban. Flip Probabilities for Random Projections of θ-separated vectors. School Technical Report CSR-10-10. pdf

R.J. Durrant and Ata Kaban. A comparison of the moments of a quadratic form involving orthonormalised and normalised random projection matrices. School Technical Report CSR-11-04. pdf

Random Projections for Machine Learning and Data Mining: Theory and Applications. ECML-PKDD 2012, Bristol. Slides (pdf) Handouts (6 Slides per page) (pdf)

Sparsity in the context of learning from high dimensional data - with Ata Kaban. ICARN International Workshop 26 Sept 2008, Liverpool. pdf

Finite Sample Effects in Compressed Fisher's LDA - with Ata Kaban. 'Breaking News' poster presented at the 13th International Conference on Artificial Intelligence and Statistics (AIStats 2010) Poster (pdf) Proof (pdf)

Compressed Fisher Linear Discriminant Analysis: Classification of Randomly Projected Data. Departmental Seminar, School of Computer Science, University of Birmingham, 10 June 2010.

Random triangles and the curse of dimensionality. Postgraduate seminar, School of Computer Science, University of Birmingham, 2 March 2011; Workshop presentation at 6th-form Mathematics Conference, King Edward's Camp Hill School for Girls, 4 March 2011.

Why thousand-dimensional vector spaces are interesting. Mathematics Teachers' Conference, School of Mathematics, University of Birmingham, 7 July 2011.

Pattern Recognition Letters (Elsevier)

Pattern Analysis and Applications (Springer)

Information Sciences (Elsevier)

Neurocomputing (Elsevier)

Transactions on Systems, Man and Cybernetics (IEEE)

Transactions on Pattern Analysis and Machine Intelligence (IEEE)

Last Revised 28/05/13