SIAM Review
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A robust minimax approach to classification
The Journal of Machine Learning Research
Comparing in situ mRNA expression patterns of drosophila embryos
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Convex Optimization
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
A fast iterative algorithm for fisher discriminant using heterogeneous kernels
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multi-task feature and kernel selection for SVMs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The Journal of Machine Learning Research
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
A statistical framework for genomic data fusion
Bioinformatics
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
A DC-programming algorithm for kernel selection
ICML '06 Proceedings of the 23rd international conference on Machine learning
Optimal kernel selection in Kernel Fisher discriminant analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
Nonstationary kernel combination
ICML '06 Proceedings of the 23rd international conference on Machine learning
Feature space perspectives for learning the kernel
Machine Learning
Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis
The Journal of Machine Learning Research
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Learning nonparametric kernel matrices from pairwise constraints
Proceedings of the 24th international conference on Machine learning
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Least squares linear discriminant analysis
Proceedings of the 24th international conference on Machine learning
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Efficient hyperkernel learning using second-order cone programming
IEEE Transactions on Neural Networks
Class Prediction from Disparate Biological Data Sources Using an Iterative Multi-Kernel Algorithm
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
An MRF-based kernel method for nonlinear feature extraction
Image and Vision Computing
Multiclass probabilistic kernel discriminant analysis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Model Selection: Beyond the Bayesian/Frequentist Divide
The Journal of Machine Learning Research
Rademacher chaos complexities for learning the kernel problem
Neural Computation
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Multiple kernel learning via distance metric learning for interactive image retrieval
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Combining multiple kernels by augmenting the kernel matrix
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Non-sparse multiple kernel fisher discriminant analysis
The Journal of Machine Learning Research
Learning low-rank Mercer kernels with fast-decaying spectrum
Neurocomputing
SPF-GMKL: generalized multiple kernel learning with a million kernels
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Domain transfer dimensionality reduction via discriminant kernel learning
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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Regularized kernel discriminant analysis (RKDA) performs linear discriminant analysis in the feature space via the kernel trick. Its performance depends on the selection of kernels. In this paper, we consider the problem of multiple kernel learning (MKL) for RKDA, in which the optimal kernel matrix is obtained as a linear combination of pre-specified kernel matrices. We show that the kernel learning problem in RKDA can be formulated as convex programs. First, we show that this problem can be formulated as a semidefinite program (SDP). Based on the equivalence relationship between RKDA and least square problems in the binary-class case, we propose a convex quadratically constrained quadratic programming (QCQP) formulation for kernel learning in RKDA. A semi-infinite linear programming (SILP) formulation is derived to further improve the efficiency. We extend these formulations to the multi-class case based on a key result established in this paper. That is, the multi-class RKDA kernel learning problem can be decomposed into a set of binary-class kernel learning problems which are constrained to share a common kernel. Based on this decomposition property, SDP formulations are proposed for the multi-class case. Furthermore, it leads naturally to QCQP and SILP formulations. As the performance of RKDA depends on the regularization parameter, we show that this parameter can also be optimized in a joint framework with the kernel. Extensive experiments have been conducted and analyzed, and connections to other algorithms are discussed.