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Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
TLDR
A new supervised dimensionality reduction algorithm called marginal Fisher analysis is proposed in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizing the interclass separability. Expand
Visual event recognition in videos by learning from web data
TLDR
A new aligned space-time pyramid matching method to measure the distances between two video clips, and a cross-domain learning method to learn an adapted classifier based on multiple base kernels and the prelearned average classifiers by minimizing both the structural risk functional and the mismatch between data distributions from two domains. Expand
Domain Transfer Multiple Kernel Learning
TLDR
Comprehensive experiments on three domain adaptation data sets demonstrate that DTMKL-based methods outperform existing cross-domain learning and multiple kernel learning methods. Expand
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
TLDR
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 by modeling the mismatch between h(X) and F. Expand
Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation
TLDR
This paper proposes a novel method called Heterogeneous Feature Augmentation (HFA) based on SVM which can simultaneously learn the target classifier as well as infer the labels of unlabeled target samples and shows that the SHFA and HFA outperform the existing HDA methods. Expand
Learning with Augmented Features for Heterogeneous Domain Adaptation
TLDR
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 different dimensions, and it is demonstrated that HFA outperforms the existing HDA methods. Expand
DVC: An End-To-End Deep Video Compression Framework
TLDR
This paper proposes the first end-to-end video compression deep model that jointly optimizes all the components for video compression, and shows that the proposed approach can outperform the widely used video coding standard H.264 in terms of PSNR and be even on par with the latest standard MS-SSIM. Expand
Domain Adaptation From Multiple Sources: A Domain-Dependent Regularization Approach
TLDR
A new framework called domain adaptation machine (DAM) is proposed for the multiple source domain adaption problem and a new domain-dependent regularizer based on smoothness assumption is proposed, which enforces that the target classifier shares similar decision values with the relevant base classifiers on the unlabeled instances from the target domain. Expand
Domain adaptation from multiple sources via auxiliary classifiers
TLDR
A new data-dependent regularizer based on smoothness assumption into Least-Squares SVM (LS-SVM), which enforces that the target classifier shares similar decision values with the auxiliary classifiers from relevant source domains on the unlabeled patterns of the target domain. Expand
Image Clustering Using Local Discriminant Models and Global Integration
TLDR
This paper proposes a new image clustering algorithm, referred to as clustering using local discriminant models and global integration (LDMGI), and shows that LDMGI shares a similar objective function with the spectral clustering (SC) algorithms, e.g., normalized cut (NCut). Expand
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