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Multiple kernel learning

About: Multiple kernel learning is a research topic. Over the lifetime, 1630 publications have been published within this topic receiving 56082 citations.


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Proceedings Article
07 Dec 2009
TL;DR: Results on real-world datasets show that the new MKL formulation is well-suited for object categorization tasks and that the MD based algorithm outperforms state-of-the-art MKL solvers like simpleMKL in terms of computational effort.
Abstract: Motivated from real world problems, like object categorization, we study a particular mixed-norm regularization for Multiple Kernel Learning (MKL). It is assumed that the given set of kernels are grouped into distinct components where each component is crucial for the learning task at hand. The formulation hence employs l∞ regularization for promoting combinations at the component level and l1 regularization for promoting sparsity among kernels in each component. While previous attempts have formulated this as a non-convex problem, the formulation given here is an instance of non-smooth convex optimization problem which admits an efficient Mirror-Descent (MD) based procedure. The MD procedure optimizes over product of simplexes, which is not a well-studied case in literature. Results on real-world datasets show that the new MKL formulation is well-suited for object categorization tasks and that the MD based algorithm outperforms state-of-the-art MKL solvers like simpleMKL in terms of computational effort.

56 citations

Proceedings ArticleDOI
24 Aug 2014
TL;DR: A multiple kernel spectral clustering algorithm is proposed that can determine the kernel weights and cluster the multi-view data simultaneously and is compared with some recent published methods on real-world datasets to show the efficiency of the proposed algorithm.
Abstract: For a given data set, exploring their multi-view instances under a clustering framework is a practical way to boost the clustering performance. This is because that each view might reflect partial information for the existing data. Furthermore, due to the noise and other impact factors, exploring these instances from different views will enhance the mining of the real structure and feature information within the data set. In this paper, we propose a multiple kernel spectral clustering algorithm through the multi-view instances on the given data set. By combining the kernel matrix learning and the spectral clustering optimization into one process framework, the algorithm can determine the kernel weights and cluster the multi-view data simultaneously. We compare the proposed algorithm with some recent published methods on real-world datasets to show the efficiency of the proposed algorithm.

56 citations

03 Mar 2014
TL;DR: In this paper, the locations of salient, discriminative video segments are treated as a latent variable, allowing the model to explicitly ignore portions of the video that are unimportant for classification.
Abstract: : We present a compositional model for video event detection. A video is modeled using a collection of both global and segment-level features and kernel functions are employed for similarity comparisons. The locations of salient, discriminative video segments are treated as a latent variable, allowing the model to explicitly ignore portions of the video that are unimportant for classification. A novel, multiple kernel learning (MKL) latent support vector machine (SVM) is defined, that is used to combine and re-weight multiple feature types in a principled fashion while simultaneously operating within the latent variable framework. The compositional nature of the proposed model allows it to respond directly to the challenges of temporal clutter and intra-class variation, which are prevalent in unconstrained internet videos. Experimental results on the TRECVID Multimedia Event Detection 2011 (MED11) dataset demonstrate the efficacy of the method.

56 citations

Journal ArticleDOI
TL;DR: This work proposes a more discriminative graph learning method which can preserve the pairwise similarities between samples in an adaptive manner for the first time and unifies clustering and graph learning which can directly obtain cluster indicators from the graph itself without performing further clustering step.

56 citations

Proceedings ArticleDOI
23 Jun 2014
TL;DR: This paper constructs a two-graph model to represent human actions by recording the spatial and temporal relationships among local features and proposes a novel family of context-dependent graph kernels (CGKs) to measure similarity between graphs.
Abstract: Graphs are a powerful tool to model structured objects, but it is nontrivial to measure the similarity between two graphs In this paper, we construct a two-graph model to represent human actions by recording the spatial and temporal relationships among local features We also propose a novel family of context-dependent graph kernels (CGKs) to measure similarity between graphs First, local features are used as the vertices of the two-graph model and the relationships among local features in the intra-frames and inter-frames are characterized by the edges Then, the proposed CGKs are applied to measure the similarity between actions represented by the two-graph model Graphs can be decomposed into numbers of primary walk groups with different walk lengths and our CGKs are based on the context-dependent primary walk group matching Taking advantage of the context information makes the correctly matched primary walk groups dominate in the CGKs and improves the performance of similarity measurement between graphs Finally, a generalized multiple kernel learning algorithm with a proposed l12-norm regularization is applied to combine these CGKs optimally together and simultaneously train a set of action classifiers We conduct a series of experiments on several public action datasets Our approach achieves a comparable performance to the state-of-the-art approaches, which demonstrates the effectiveness of the two-graph model and the CGKs in recognizing human actions

55 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202321
202244
202172
2020101
2019113
2018114