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Robust and Scalable GraphBased Semisupervised Learning
, 2012
"... Graphbased semisupervised learning (GSSL) provides a promising paradigm for modeling the manifold structures that may exist in massive data sources in highdimensional spaces. It has been shown effective in propagating a limited amount of initial labels to a large amount of unlabeled data, matching ..."
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Cited by 14 (7 self)
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Graphbased semisupervised learning (GSSL) provides a promising paradigm for modeling the manifold structures that may exist in massive data sources in highdimensional spaces. It has been shown effective in propagating a limited amount of initial labels to a large amount of unlabeled data, matching the needs of many emerging applications such as image annotation and information retrieval. In this paper, we provide reviews of several classical GSSL methods and a few promising methods in handling challenging issues often encountered in webscale applications. First, to successfully incorporate the contaminated noisy labels associated with web data, label diagnosis and tuning techniques applied to GSSL are surveyed. Second, to support scalability to the gigantic scale (millions or billions of samples), recent solutions based on anchor graphs are reviewed. To help researchers pursue new ideas in this area, we also summarize a few popular data sets and software tools publicly available. Important open issues are discussed at the end to stimulate future research.
A Unifying Framework for Vectorvalued Manifold Regularization and Multiview Learning
"... This paper presents a general vectorvalued reproducing kernel Hilbert spaces (RKHS) formulation for the problem of learning an unknown functional dependency between a structured input space and a structured output space, in the SemiSupervised Learning setting. Our formulation includes as special c ..."
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Cited by 8 (5 self)
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This paper presents a general vectorvalued reproducing kernel Hilbert spaces (RKHS) formulation for the problem of learning an unknown functional dependency between a structured input space and a structured output space, in the SemiSupervised Learning setting. Our formulation includes as special cases Vectorvalued Manifold Regularization and Multiview Learning, thus provides in particular a unifying framework linking these two important learning approaches. In the case of least square loss function, we provide a closed form solution with an efficient implementation. Numerical experiments on challenging multiclass categorization problems show that our multiview learning formulation achieves results which are comparable with state of the art and are significantly better than singleview learning. 1.
LargeScale Machine Learning for Classification and Search
, 2012
"... With the rapid development of the Internet, nowadays tremendous amounts of data including images and videos, up to millions or billions, can be collected for training machine learning models. Inspired by this trend, this thesis is dedicated to developing largescale machine learning techniques for t ..."
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Cited by 2 (1 self)
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With the rapid development of the Internet, nowadays tremendous amounts of data including images and videos, up to millions or billions, can be collected for training machine learning models. Inspired by this trend, this thesis is dedicated to developing largescale machine learning techniques for the purpose of making classification and nearest neighbor search practical on gigantic databases. Our first approach is to explore data graphs to aid classification and nearest neighbor search. A graph offers an attractive way of representing data and discovering the essential information such as the neighborhood structure. However, both of the graph construction process and graphbased learning techniques become computationally prohibitive at a large scale. To this end, we present an efficient large graph construction approach and subsequently apply it to develop scalable semisupervised learning and unsupervised hashing algorithms. Our unique contributions on the graphrelated topics include: 1. Large Graph Construction: Conventional neighborhood graphs such as kNN graphs require a quadratic time complexity, which is inadequate for largescale applications mentioned above. To overcome this bottleneck, we present a novel graph construction approach,
Online Learning with Multiple Operatorvalued Kernels
"... We consider the problem of learning a vectorvalued function f in an online learning setting. The function f is assumed to lie in a reproducing Hilbert space of operatorvalued kernels. We describe two online algorithms for learning f while taking into account the output structure. A first contri ..."
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Cited by 1 (0 self)
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We consider the problem of learning a vectorvalued function f in an online learning setting. The function f is assumed to lie in a reproducing Hilbert space of operatorvalued kernels. We describe two online algorithms for learning f while taking into account the output structure. A first contribution is an algorithm, ONORMA, that extends the standard kernelbased online learning algorithm NORMA from scalarvalued to operatorvalued setting. We report a cumulative error bound that holds both for classification and regression. We then define a second algorithm, MONORMA, which addresses the limitation of predefining the output structure in ONORMA by learning sequentially a linear combination of operatorvalued kernels. Our experiments show that the proposed algorithms achieve good performance results with low computational cost. 1
Journal of Machine Learning Research 1x (201x) xxx Submitted x/0x; Published x/0x A Unifying Framework in Vectorvalued Reproducing Kernel Hilbert Spaces for Manifold Regularization and CoRegularized Multiview Learning
"... This paper presents a general vectorvalued reproducing kernel Hilbert spaces (RKHS) framework for the problem of learning an unknown functional dependency between a structured input space and a structured output space. Our formulation encompasses both Vectorvalued Manifold Regularization and Cor ..."
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This paper presents a general vectorvalued reproducing kernel Hilbert spaces (RKHS) framework for the problem of learning an unknown functional dependency between a structured input space and a structured output space. Our formulation encompasses both Vectorvalued Manifold Regularization and Coregularized Multiview Learning, providing in particular a unifying framework linking these two important learning approaches. In the case of the least square loss function, we provide a closed form solution, which is obtained by solving a system of linear equations. In the case of Support Vector Machine (SVM) classification, our formulation generalizes in particular both the binary Laplacian SVM to the multiclass, multiview settings and the multiclass Simplex Cone SVM to the semisupervised, multiview settings. The solution is obtained by solving a single quadratic optimization problem, as in standard SVM, via the Sequential Minimal Optimization (SMO) approach. Empirical results obtained on the task of object recognition, using several challenging datasets, demonstrate the competitiveness of our algorithms compared with other stateoftheart methods.