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Learning Efficient Convolutional Networks through Network Slimming
TLDR
The approach is called network slimming, which takes wide and large networks as input models, but during training insignificant channels are automatically identified and pruned afterwards, yielding thin and compact models with comparable accuracy. Expand
Label Propagation through Linear Neighborhoods
TLDR
A novel graph-based semi supervised learning approach is proposed based on a linear neighborhood model, which assumes that each data point can be linearly reconstructed from its neighborhood, and can propagate the labels from the labeled points to the whole data set using these linear neighborhoods with sufficient smoothness. Expand
Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs
TLDR
A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI) that were higher than those using a widely used fast Fourier transform (FFT)-based spectrum estimation method. Expand
A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems
TLDR
A General Iterative Shrinkage and Thresholding (GIST) algorithm to solve the nonconvex optimization problem for a large class of non-conveX penalties and a detailed convergence analysis of the GIST algorithm is presented. 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
Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs
TLDR
A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI) that were higher than those using a widely used FFT (fast Fourier transform)-based spectrum estimation method. Expand
Label Propagation through Linear Neighborhoods
TLDR
A novel graph-based semi supervised learning approach is proposed based on a linear neighborhood model, which assumes that each data point can be linearly reconstructed from its neighborhood, and can propagate the labels from the labeled points to the whole data set using these linear neighborhoods with sufficient smoothness. Expand
Discriminative Least Squares Regression for Multiclass Classification and Feature Selection
TLDR
The core idea is to enlarge the distance between different classes under the conceptual framework of LSR, and a technique called ε-dragging is introduced to force the regression targets of different classes moving along opposite directions such that the distances between classes can be enlarged. Expand
Manifold-ranking based image retrieval
TLDR
MRBIR first makes use of a manifold ranking algorithm to explore the relationship among all the data points in the feature space, and then measures relevance between the query and all the images in the database accordingly, which is different from traditional similarity metrics based on pair-wise distance. Expand
Learning a Mahalanobis distance metric for data clustering and classification
TLDR
This paper considers a general problem of learning from pairwise constraints in the form of must-links and cannot-links, and aims to learn a Mahalanobis distance metric. Expand
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