Journal ArticleDOI
Unsupervised non-parametric kernel learning algorithm
Bing Liu,Shi-Xiong Xia,Yong Zhou +2 more
TLDR
Experimental results show that the proposed unsupervised non-parametric kernel learning algorithm is significantly more effective and applicable to enhance the performance of Maximum Margin Clustering (MMC).Abstract:
A foundational problem in kernel-based learning is how to design suitable kernels. Non-Parametric Kernel Learning (NPKL) is one of the most important kernel learning methods. However, most research on NPKL has tended to focus on the semi-supervised scenario. In this paper, we propose a novel unsupervised non-parametric kernel learning method, which can seamlessly combine the spectral embedding of unlabeled data and manifold Regularized Least-Squares (RLS) to learn non-parametric kernels efficiently. The proposed algorithm enjoys a closed-form solution in each iteration, which can be efficiently computed by the Lanczos sparse eigen-decomposition technique. Meanwhile, it can be extended to supervised kernel learning naturally. Experimental results show that our proposed unsupervised non-parametric kernel learning algorithm is significantly more effective and applicable to enhance the performance of Maximum Margin Clustering (MMC). Especially, it outperforms multiple kernel learning in both unsupervised and supervised settings.read more
Citations
More filters
Journal ArticleDOI
Spectral co-clustering ensemble
TL;DR: This is the first work on using spectral algorithm for co-clustering ensemble using matrix decomposition based approach which can be formulated as a bipartite graph partition problem and solve it efficiently with the selected eigenvectors.
Journal ArticleDOI
Two-stage multiple kernel learning with multiclass kernel polarization
TL;DR: This paper proposes a simple but effective multiclass MKL method by a two-stage strategy, in which the first stage finds the kernel weights to combine the kernels, and the second stage trains a standard multiclass support vector machine (SVM).
Book ChapterDOI
Semi-supervised low rank kernel learning algorithm via extreme learning machine
TL;DR: A more efficient semi-supervised NPKL method, which can effectively learn a low-rank kernel matrix from must-link and cannot-link constraints and can be solved much more efficiently than SDP solvers.
Journal ArticleDOI
Efficient karaoke song recommendation via multiple kernel learning approximation
TL;DR: This paper develops a karaoke recommender system by taking into account vocal competence, and develops the SMO method for optimizing the MKLA dual and presents a theoretical analysis to show the lower bound of the method.
Kernel-based clustering of big data
TL;DR: An approximate kernel-based clustering algorithm is developed, which uses a low-rank approximate kernel matrix, constructed from a uniformly sampled small s ubset of the data, to perform clustering and achieves high cluster quality, when provided with sufficient number of data samples.
References
More filters
Journal ArticleDOI
Extreme Learning Machine for Regression and Multiclass Classification
TL;DR: ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly and in theory, ELM can approximate any target continuous function and classify any disjoint regions.
Journal Article
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
TL;DR: A semi-supervised framework that incorporates labeled and unlabeled data in a general-purpose learner is proposed and properties of reproducing kernel Hilbert spaces are used to prove new Representer theorems that provide theoretical basis for the algorithms.
Proceedings Article
Semi-supervised learning using Gaussian fields and harmonic functions
TL;DR: An approach to semi-supervised learning is proposed that is based on a Gaussian random field model, and methods to incorporate class priors and the predictions of classifiers obtained by supervised learning are discussed.
Journal ArticleDOI
Learning the Kernel Matrix with Semidefinite Programming
Gert R. G. Lanckriet,Nello Cristianini,Peter L. Bartlett,Laurent El Ghaoui,Michael I. Jordan +4 more
TL;DR: This paper shows how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques and leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.
Proceedings ArticleDOI
Training linear SVMs in linear time
TL;DR: A Cutting Plane Algorithm for training linear SVMs that provably has training time 0(s,n) for classification problems and o(sn log (n)) for ordinal regression problems and several orders of magnitude faster than decomposition methods like svm light for large datasets.