Tensor LRR and Sparse Coding-Based Subspace Clustering

Fu, Yifan; Gao, Junbin; Tien, David; Lin, Zhouchen; Hong, Xia

doi:10.1109/tnnls.2016.2553155

Cited by 57 publications

(18 citation statements)

References 48 publications

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“…Sampling, Sparse & Low Rank : [30] [31] Dimension of subspace need to be known and equal Complexity is exponential to the number of subspace dimension they can handle noise and outliers in data They do not need to know the dimension ( No of sub-spaces a priori)…”

Section: Proposed Methodologymentioning

confidence: 99%

RMSC: Robust modeling of subspace clustering for high dimensional data

Radhika

et al. 2017

2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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Abstract-Subspace clustering is one of the active research problem associated with high-dimensional data. Here some of the standard techniques are reviewed to investigate existing methodologies. Although, there have been various forms of research techniques evolved recently, they do not completely mitigate the problems pertaining to noise sustainability and optimization of clustering accuracy. Hence, a novel technique called as Robust Modeling of Subspace Clustering (RMSC) presented to solve the above problem. An analytical research methodology is used to formulate two algorithms for computing outliers and for extracting elite subspace from the highdimensional data inflicted by different forms of noise. RMSC was found to offer higher accuracy and lower error rate both in presence of noise and absence of noise over high-dimensional data.

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Section: Proposed Methodologymentioning

confidence: 99%

RMSC: Robust modeling of subspace clustering for high dimensional data

Radhika

et al. 2017

2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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“…color images, videos, hyper-spectral images, high-dynamical range images, 3D range data etc.) [24], [25], [26], where important structures or useful information will be lost if we process them as a 1-D signal or a 2D matrix. These data are often subject to all types of geometric deformation or corruptions due to change of viewpoints, illuminations or occlusions.…”

Section: Introductionmentioning

confidence: 99%

Robust Low-Rank Tensor Recovery with Rectification and Alignment

Zhang

Wang

Zhou

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

158

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Low-rank tensor recovery in the presence of sparse but arbitrary errors is an important problem with many practical applications. In this work, we propose a general framework that recovers low-rank tensors, in which the data can be deformed by some unknown transformation and corrupted by arbitrary sparse errors. We present a unified presentation of the surrogate-based formulations that incorporate the feacture of rectification and alignment simultaneously, and establish worst-case error bounds of the recovered tensor. In this context, the state-of-the-art methods "RASL" and "TILT" can be viewed as two special cases of our work, and yet each only performs part of the function of our method. Subsequently, we study the optimization aspects of the problem in detail by deriving two algorithms, one based on ADMM and the other based on proximal gradient. We provide global optimality convergence guarantees for the latter algorithm, and demonstrate the performance of the former through in-depth simulations. Finally, we present extensive experimental results on public datasets to demonstrate the efficacy of our proposed framework and algorithms.

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“…Fu. et al [29] proposed a tensor low-rank representation and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures.…”

Section: Introductionmentioning

confidence: 99%

Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction

Lei

Song

et al. 2019

Remote Sensing

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Dimensionality reduction is an essential and important issue in hyperspectral image processing. With the advantages of preserving the spatial neighborhood information and the global structure information, tensor analysis and low rank representation have been widely considered in this field and yielded satisfactory performance. In available tensor- and low rank-based methods, how to construct appropriate tensor samples and determine the optimal rank of hyperspectral images along each mode are still challenging issues. To address these drawbacks, an unsupervised tensor-based multiscale low rank decomposition (T-MLRD) method for hyperspectral images dimensionality reduction is proposed in this paper. By regarding the raw cube hyperspectral image as the only tensor sample, T-MLRD needs no labeled samples and avoids the processing of constructing tensor samples. In addition, a novel multiscale low rank estimating method is proposed to obtain the optimal rank along each mode of hyperspectral image which avoids the complicated rank computing. Finally, the multiscale low rank feature representation is fused to achieve dimensionality reduction. Experimental results on real hyperspectral datasets demonstrate the superiority of the proposed method over several state-of-the-art approaches.

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Tensor LRR and Sparse Coding-Based Subspace Clustering

Cited by 57 publications

References 48 publications

RMSC: Robust modeling of subspace clustering for high dimensional data

RMSC: Robust modeling of subspace clustering for high dimensional data

Robust Low-Rank Tensor Recovery with Rectification and Alignment

Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction

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