Low-rank and sparse representationDictionary learningImage fusion can integrate the complementary information of multiple images. However, when the images to be fused are damaged, the existing fusion methods cannot recover the lost information. Matrix completion, on the other hand, can be used to ...
研究者提出了一种新颖的算法,利用这种表示方法对高光谱图像进行分析,并识别出与背景不同的异常区域。实验结果表明,这种方法在处理高维数据和复杂背景下具有很好的性能,为高光谱图像中的异常检测问题提供了一种有效且可靠的解决方案。Anomaly-Detection-in-Hyperspectral-Images-Based-on-Low-Rank-and-Sparse-Representation...
In order to improve this respect, we propose a new subspace transfer learning algorithm, namely Laplacian Regularized Low-Rank Sparse Representation Transfer Learning (LRLRSR-TL). After introducing the low-rank representation and sparse constraints, the method incorporates Laplacian regularization term to...
BLSE has two steps, i.e., block-diagonal constrained low-rank and sparse representation (BLSR) and block-diagonal constrained low-rank and sparse graph embedding (BLSGE). Firstly, the BLSR model is developed to reveal the intrinsic intra...
Sparse representation and low-rank approximation are fundamental tools in fields as diverse as computer vision, computational biology, signal processing, natural language processing, and machine learning. Recent advances in sparse and low-rank modeling have led to increasingly concise descriptions of high...
A novel method for anomaly detection in hyperspectral images (HSIs) is proposed based on low-rank and sparse representation. The proposed method is based on the separation of the background and the anomalies in the observed data. Since each pixel in the background can be approximately represente...
[16] developed a low-rank and sparse representation (LRSR) method. Further, Wang et al. [17] introduced a stable multi-subspace learning method (SMSL). Compared with matrix, tensor can use the correlation information between different patches. Therefore, Dai et al. [18] first extended IPI ...
Xu Y, Fang X, Wu J, Li X, Zhang D (2015) Discriminative transfer subspace learning via low-rank and sparse representation. IEEE Trans Image Process 25:850–863 Article MathSciNet Google Scholar Zhang T, Ghanem B, Liu S, Ahuja (2012)Low-rank sparse learning for robust visual tracking....
Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models—those that emphasize problem-specific Interpretability—with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit...
Robust Adaptive Low-Rank and Sparse Embedding for Feature Representation Lei Wang※ , Zhao Zhang ※, * , Senior Member, IEEE, Guangcan Liu ☆ , Member, IEEE, Qiaolin Ye ? , Member, IEEE, Jie Qin § , Member, IEEE, and Meng Wang∮ , Senior Member, IEEE ※ School of Computer Science...