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Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization
TLDR
This paper proposes to achieve a better approximation to the rank of matrix by truncated nuclear norm, which is given by the nuclear norm subtracted by the sum of the largest few singular values, and develops a novel matrix completion algorithm by minimizing the Truncated Nuclear Norm. Expand
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial
TLDR
This paper presents a systematic framework to decompose big data systems into four sequential modules, namely data generation, data acquisition, data storage, and data analytics, and presents the prevalent Hadoop framework for addressing big data challenges. Expand
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
TLDR
A general tensor discriminant analysis (GTDA) is developed as a preprocessing step for LDA for face recognition and achieves good performance for gait recognition based on image sequences from the University of South Florida (USF) HumanID Database. Expand
PixelLink: Detecting Scene Text via Instance Segmentation
Most state-of-the-art scene text detection algorithms are deep learning based methods that depend on bounding box regression and perform at least two kinds of predictions: text/non-textExpand
Constrained Nonnegative Matrix Factorization for Image Representation
TLDR
It is shown how explicitly combining label information improves the discriminating power of the resulting matrix decomposition, and the effectiveness of the novel algorithm in comparison to the state-of-the-art approaches through a set of evaluations based on real-world applications. Expand
Manifold Regularized Sparse NMF for Hyperspectral Unmixing
TLDR
Manifold regularization is incorporated into sparsity-constrained NMF for unmixing in this paper and can keep the close link between the original image and the material abundance maps, which leads to a more desired un Mixing performance. Expand
Patch Alignment for Dimensionality Reduction
TLDR
A new dimensionality reduction algorithm is developed, termed discrim inative locality alignment (DLA), by imposing discriminative information in the part optimization stage, and thorough empirical studies demonstrate the effectiveness of DLA compared with representative dimensionality Reduction algorithms. Expand
Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval
TLDR
An asymmetric bagging and random subspace SVM (ABRS-SVM) is built to solve three problems and further improve the relevance feedback performance. Expand
A survey of graph edit distance
TLDR
The research advance of G ED is surveyed in order to provide a review of the existing literatures and offer some insights into the studies of GED. Expand
Computer vision and pattern recognition
TLDR
This Special Issue of International Journal of Computer Mathematics (IJCM) offers a venue to present innovative approaches in computer vision and pattern recognition, which have been changing the authors' everyday life dramatically over the last few years, and aims to provide readers with cutting-edge and topical information for their related research. Expand
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