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Kernel principal component analysis
Advances in kernel methods
Least Squares Support Vector Machine Classifiers
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Proximal support vector machine classifiers
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SMO algorithm for least-squares SVM formulations
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Ridge Regression Learning Algorithm in Dual Variables
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Learning the Kernel Matrix with Semi-Definite Programming
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Learning the Kernel Matrix with Semidefinite Programming
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ICML '04 Proceedings of the twenty-first international conference on Machine learning
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One-Shot Learning of Object Categories
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Generalized Discriminant Analysis Using a Kernel Approach
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Optimal kernel selection in Kernel Fisher discriminant analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
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Large Scale Multiple Kernel Learning
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Multi-class Discriminant Kernel Learning via Convex Programming
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An Automated Combination of Kernels for Predicting Protein Subcellular Localization
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Efficient Kernel Discriminant Analysis via Spectral Regression
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
On Relevant Dimensions in Kernel Feature Spaces
The Journal of Machine Learning Research
Kernel Codebooks for Scene Categorization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Automated Flower Classification over a Large Number of Classes
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
A Comparison of L_1 Norm and L_2 Norm Multiple Kernel SVMs in Image and Video Classification
CBMI '09 Proceedings of the 2009 Seventh International Workshop on Content-Based Multimedia Indexing
Non-sparse Multiple Kernel Learning for Fisher Discriminant Analysis
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
L2 regularization for learning kernels
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
The SHOGUN Machine Learning Toolbox
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Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
First and Second Order SMO Algorithms for LS-SVM Classifiers
Neural Processing Letters
lp-Norm Multiple Kernel Learning
The Journal of Machine Learning Research
Probabilistic classifiers with a generalized Gaussian scale mixture prior
Pattern Recognition
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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 lp 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 in MKL 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 lp MK-FDA, fixed norm MK-FDA, and several variants of SVM-based MKL (MK-SVM). Our results demonstrate that lp MK-FDA improves upon sparse MK-FDA in many practical situations. The results also show that on image categorisation problems, lp 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.