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Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
A large family of algorithms - supervised or unsupervised; stemming from statistics or geometry theory - has been designed to provide different solutions to the problem of dimensionality reduction.Expand
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Visual event recognition in videos by learning from web data
We propose a visual event recognition framework for consumer domain videos by leveraging a large amount of loosely labeled web videos (e.g., from YouTube). First, we propose a new aligned space-timeExpand
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Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the new data points.Expand
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Learning with Augmented Features for Heterogeneous Domain Adaptation
We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features with differentExpand
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Domain Transfer Multiple Kernel Learning
Cross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has only a limited numberExpand
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Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation
In this paper, we study the heterogeneous domain adaptation (HDA) problem, in which the data from the source domain and the target domain are represented by heterogeneous features with differentExpand
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Domain adaptation from multiple sources via auxiliary classifiers
We propose a multiple source domain adaptation method, referred to as Domain Adaptation Machine (DAM), to learn a robust decision function (referred to as target classifier) for label prediction ofExpand
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Multilinear Discriminant Analysis for Face Recognition
There is a growing interest in subspace learning techniques for face recognition; however, the excessive dimension of the data space often brings the algorithms into the curse of dimensionalityExpand
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Domain Adaptation From Multiple Sources: A Domain-Dependent Regularization Approach
In this paper, we propose a new framework called domain adaptation machine (DAM) for the multiple source domain adaption problem. Under this framework, we learn a robust decision function (referredExpand
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Marginal Fisher Analysis and Its Variants for Human Gait Recognition and Content- Based Image Retrieval
Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for human gait recognition and content-based imageExpand
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