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Learning Efficient Convolutional Networks through Network Slimming
The deployment of deep convolutional neural networks (CNNs) in many real world applications is largely hindered by their high computational cost. In this paper, we propose a novel learning scheme forExpand
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Label Propagation through Linear Neighborhoods
In many practical data mining applications such as text classification, unlabeled training examples are readily available, but labeled ones are fairly expensive to obtain. Therefore, semi supervisedExpand
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Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs
Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is toExpand
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A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems
Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over the convexExpand
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Manifold-ranking based image retrieval
In this paper, we propose a novel transductive learning framework named manifold-ranking based image retrieval (MRBIR). Given a query image, MRBIR first makes use of a manifold ranking algorithm toExpand
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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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Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs
Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is toExpand
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Label Propagation through Linear Neighborhoods
  • Fei Wang, C. Zhang
  • Computer Science
  • IEEE Transactions on Knowledge and Data…
  • 25 June 2006
In many practical data mining applications such as text classification, unlabeled training examples are readily available, but labeled ones are fairly expensive to obtain. Therefore, semi supervisedExpand
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Discriminative Least Squares Regression for Multiclass Classification and Feature Selection
This paper presents a framework of discriminative least squares regression (LSR) for multiclass classification and feature selection. The core idea is to enlarge the distance between differentExpand
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Learning a Mahalanobis distance metric for data clustering and classification
Distance metric is a key issue in many machine learning algorithms. This paper considers a general problem of learning from pairwise constraints in the form of must-links and cannot-links. As oneExpand
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