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ICASSP
2011
IEEE
10 years 9 months ago
Multiple kernel nonnegative matrix factorization
Kernel nonnegative matrix factorization (KNMF) is a recent kernel extension of NMF, where matrix factorization is carried out in a reproducing kernel Hilbert space (RKHS) with a f...
Shounan An, Jeong-Min Yun, Seungjin Choi
PAMI
2011
11 years 5 days ago
Multiple Kernel Learning for Dimensionality Reduction
—In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting ...
Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
CORR
2010
Springer
148views Education» more  CORR 2010»
11 years 5 days ago
A Unifying View of Multiple Kernel Learning
Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different for...
Marius Kloft, Ulrich Rückert, Peter L. Bartle...
ICCV
2009
IEEE
11 years 3 months ago
Selection and context for action recognition
Recognizing human action in non-instrumented video is a challenging task not only because of the variability produced by general scene factors like illumination, background, occlu...
Dong Han, Liefeng Bo, Cristian Sminchisescu
ICDM
2010
IEEE
187views Data Mining» more  ICDM 2010»
11 years 3 months ago
Financial Forecasting with Gompertz Multiple Kernel Learning
Financial forecasting is the basis for budgeting activities and estimating future financing needs. Applying machine learning and data mining models to financial forecasting is both...
Han Qin, Dejing Dou, Yue Fang
CVPR
2010
IEEE
11 years 3 months ago
Visual classification with multi-task joint sparse representation
We address the problem of computing joint sparse representation of visual signal across multiple kernel-based representations. Such a problem arises naturally in supervised visual...
Xiaotong Yuan, Shuicheng Yan
ICMLC
2010
Springer
11 years 3 months ago
Multiple kernel learning and feature space denoising
We review a multiple kernel learning (MKL) technique called p regularised multiple kernel Fisher discriminant analysis (MK-FDA), and investigate the effect of feature space denois...
Fei Yan, Josef Kittler, Krystian Mikolajczyk
JMLR
2006
156views more  JMLR 2006»
11 years 5 months ago
Large Scale Multiple Kernel Learning
While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic ...
Sören Sonnenburg, Gunnar Rätsch, Christi...
BMCBI
2010
182views more  BMCBI 2010»
11 years 5 months ago
L2-norm multiple kernel learning and its application to biomedical data fusion
Background: This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields diff...
Shi Yu, Tillmann Falck, Anneleen Daemen, Lé...
NIPS
2008
11 years 6 months ago
An Extended Level Method for Efficient Multiple Kernel Learning
We consider the problem of multiple kernel learning (MKL), which can be formulated as a convex-concave problem. In the past, two efficient methods, i.e., Semi-Infinite Linear Prog...
Zenglin Xu, Rong Jin, Irwin King, Michael R. Lyu
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