鲁棒核稀疏子空间聚类模型(Robust Kernel Sparse Subspace Clustering, RKSSC) 引言 鲁棒核稀疏子空间聚类模型(RKSSC)是一种用于处理高维数据的聚类技术,特别设计用于对抗数据中的噪声和异常值。 该模型结合了稀疏表示、核方法和鲁棒优化策略,以在非线性子空间中寻找数据点的稀疏表示,同时最小化噪声和异常值的影响。 原
Sparse subspace clustering (SSC) has been widely employed in machine learning and pattern recognition, but it still faces scalability challenges when dealing with large-scale datasets. Recently, stochastic SSC (SSSC) has emerged as an effective solution by leveraging the dropout technique. However, ...
Candes. Robust subspace clustering. The Annals of Statistics, 42(2) :669-699, 2014.M. Soltanolkotabi, E. Elhamifar, and E. Candes. Robust subspace clustering. The Annals of Statis- tics, 42(2):669-699, 2014.M. Soltanolkotabi, E. Elhamifar, E. J. Candes, et al., Robust ...
In the past few years, sparse representation based method has been used in many fields with breathtaking speed due to its superior sparse recovery performance. Sparse subspace clustering (SSC), as one of its application hot-spots, has attracted considerable attention. Traditional sparse subspace clust...
Kernel sparse subspace clustering on symmetric positive definite manifolds 2016, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Robust Point Set Matching for Partial Face Recognition 2016, IEEE Transactions on Image Processing Face recognition: Challenges, achiev...
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3d ux-net: a large kernel volumetric convnet modernizing hierarchical transformer for medical image segmentation [Paper] stacking ensemble learning in deep domain adaptation for ophthalmic image classification [Paper] identifying differential equations to predict blood glucose using sparse identification...
of data in subspace. To this end, Zhou et al. [17] combine LPP and FKM to learn the spatial structure representation and obtain a sparse partition matrix for better clustering. The approaches described above are common dimensionality reduction techniques and their implementation on clustering. ...
subspace. The reconstruction of the extracted noisy patches is performed by sparse representation using two dictionaries built with the DCT. The weighted encoding with the sparse non-local regularization technique (WESNR) was also applied in71to cope with mixed noise. The noise-corrupted image ...
In this paper, by integrating the correntropy induced loss into the ELM instead of the original L2-norm, an integrated model named correntropy induced loss based sparse robust graph regularized extreme learning machine (CSRGELM) is proposed. Different from the traditional ELM, we use L2,1-norm ...