Imperial College London


Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Reader in Computer Vision



+44 (0)20 7594 6220k.mikolajczyk




Electrical EngineeringSouth Kensington Campus






BibTex format

author = {Yan, F and Kittler, J and Mikolajczyk, K and Tahir, A},
doi = {10.1109/ICDM.2009.84},
journal = {Journal of Machine Learning Research},
pages = {607--642},
title = {Non-Sparse Multiple Kernel Fisher Discriminant Analysis},
url = {},
volume = {13},
year = {2011}

RIS format (EndNote, RefMan)

AB - Sparsity-inducing multiple kernel Fisher discriminant analysis (MK-FDA) has been studied in the literature. Building on recent advances in non-sparse multiple kernel learning (MKL), we propose a non-sparse version of MK-FDA, which imposes a general ‘p norm regularisation on the kernel weights. We formulate the associated optimisation problem as a semi-infinite program (SIP), and adapt an iterative wrapper algorithm to solve it. We then discuss, in light of latest advances inMKL optimisation techniques, several reformulations and optimisation strategies that can potentially lead to significant improvements in the efficiency and scalability of MK-FDA. We carry out extensive experiments on six datasets from various application areas, and compare closely the performance of ‘p MK-FDA, fixed norm MK-FDA, and several variants of SVM-based MKL (MK-SVM). Our results demonstrate that ‘p MK-FDA improves upon sparse MK-FDA in many practical situations. The results also show that on image categorisation problems, ‘p MK-FDA tends to outperform its SVM counterpart. Finally, we also discuss the connection between (MK-)FDA and (MK-)SVM, under the unified framework of regularised kernel machines.
AU - Yan,F
AU - Kittler,J
AU - Mikolajczyk,K
AU - Tahir,A
DO - 10.1109/ICDM.2009.84
EP - 642
PY - 2011///
SN - 1532-4435
SP - 607
TI - Non-Sparse Multiple Kernel Fisher Discriminant Analysis
T2 - Journal of Machine Learning Research
UR -
VL - 13
ER -