lp-Norm Multiple Kernel Learning

By Marius Kloft, Ulf Brefeld, Sören Sonnenburg, Alexander Zien. 2011

338 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification (2015, methodology): “…and the prediction outputs are averaged; (6) Multiple Kernel Learning (SVM-MKL) , which combines the two features with the (cid:96) p -norm MKL [20] by ﬁxing p = 2; (7) Early Fusion with Neural Networks (NN-EF) , which concatenates the two features into a long vector and then use a neural…”
Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks (2015, methodology): “MKL was reported to produce better performance in some cases, but the gain was also often observed to be insigniﬁcant [19].”
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality (2012, methodology): “In addition, we provide generalization analysis in terms of bounds on the Rademacher complexity of our vector-valued hypothesis spaces, complementing analogous results in the scalar multiple kernel learning literature [11, 20].”
Fusing Multi-Stream Deep Networks for Video Classification (2015, methodology): “To validate the effectiveness of our adaptive multi-stream fusion method, we compare with the following alternatives: (1) Average Fusion, where the mean scores of multiple networks are used as the final prediction; (2) Weighted Fusion, where the scores are fused linearly with weights estimated by cr”
Multi-Task Multiple Kernel Relationship Learning (2016, background): “There is a large body of work in the recent years addressing several aspects of this problem, such as efficient learning of the kernel weights, fast optimization and providing better theoretical guarantees [2, 4, 11, 12, 15, 18, 22].”
Enhanced representation and multi-task learning for image annotation (2013, methodology): “Boosting or Multiple Kernel Learning (MKL) [36] would be a natural candidate.”
Insights from Classifying Visual Concepts with Multiple Kernel Learning (2011, methodology): “For such an example, see the toy experiment of [21], which is replicated in the supplemental material including additional analysis.”
Multiple Operator-valued Kernel Learning (2012, methodology): “Since these seminal works, many efforts have been carried out in order to theoretically analyze the kernel learning framework [9, 3] or in order to provide efficient algorithms [23, 1, 14].”
Learning Kernels with Random Features (2016, background): “…or classes of kernels and either uses heuristic rules to combine kernels [2] [23], optimizes structured (e.g. linear, nonnegative, convex) compositions of the kernels with respect to an alignment metric [9] [16] [20] [28], or jointly optimizes kernel compositions with empirical risk [17] [20] [29].”
RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach (2016, background): “The other implementation of feature-level fusion is the multiple-kernel learning, which design multiple kernels for different features, and then combine them together [14, 15].”
Data Fusion by Matrix Factorization (2013, background): “p -norm MKL, see [47], [48], [11], [49]) and K”
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets (2014, background): “One popular paradigm to address the latter problem is multiple kernel learning (MKL) [27, 1, 12, 25].”
Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] (2016, background): “[150] extended MKL to arbitrary norms to allow for robust kernel mixtures that generalize well.”
Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data (2017, background): “This makes it difficult to evaluate weight coefficients [21].”