Robust Subspace Clustering With Compressed Data

Liu, Guangcan; Zhang, Zhao; Liu, Qingshan; Xiong, Hongkai

doi:10.1109/tip.2019.2917857

Cited by 56 publications

(26 citation statements)

References 40 publications

(44 reference statements)

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“…According to the quantities from formula (19), 1(H) and 2(H) require that the value of D 2 (Cl, Cl') must be nonnegative.…”

Section: Our Algorithm For Coding Pattern Optimizationmentioning

confidence: 99%

“…The SSC algorithm has been successfully applied to cluster spectral images obtained by a compressed spectral imaging (CSI) system, which needs far fewer sampling resources compared to traditional spectral imaging sensors. Several approaches can be utilized to complete spectral image clustering based on compressed data [19][20][21][22]. The relevant authors designed a down-sampling strategy to extract samples, whose probability is inversely proportional to the number of samples in their own subspaces, and combined the strengths of different methods.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Remote Sensing Images Through Reweighted Sparse Subspace Representation Using Compressed Data

Zhu¹,

Zhao²,

Wu³

2021

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In many real-world scenarios, subspace clustering essentially aims to cluster unlabeled high-dimensional data into a union of finite-dimensional linear subspaces. The problem is that the data are always high-dimensional, with the increase of the computation, storge, and communication of various intelligent data-driven systems. This paper attempts to develop a method to cluster spectral images directly using the measurements of compressive coded aperture snapshot spectral imager (CASSI), eliminating the need to reconstruct the entire data cube. Assuming that compressed measurements are drawn from multiple subspaces, a novel algorithm was developed by solving a 1-norm minimization problem, which is known as reweighted sparse subspace clustering (RSSC). The proposed algorithm clusters the compressed measurements into different subspaces, which greatly improves the clustering accuracy over the SSC algorithm by adding a reweighted step. The compressed CASSI measurements obtained using the coherence-based coded aperture can improve the performance of the proposed spectral image clustering method. The accuracy of our spectral image clustering approach was verified through simulations on two real datasets.

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“…According to the quantities from formula (19), 1(H) and 2(H) require that the value of D 2 (Cl, Cl') must be nonnegative.…”

Section: Our Algorithm For Coding Pattern Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of Remote Sensing Images Through Reweighted Sparse Subspace Representation Using Compressed Data

Zhu¹,

Zhao²,

Wu³

2021

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show abstract

“…If a signal is sparse, or a transform domain is sparse, an observation matrix unrelated to the transform basis can be used to ''compress'' the sparse high-dimensional signal into low-dimensional signal, which directly perceives the compressed data information and breaks the constraints of Shannon-Nyquist theorem. Alternatively, given only the compressed data and sensing matrix, the proposed method, row space pursuit (RSP), recovers the authentic row space that gives correct clustering results under certain conditions [33].…”

Section: A Compressed Sensing Theorymentioning

confidence: 99%

Identification of Underwater Targets Based on Sparse Representation

2020

IEEE Access

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We consider using sparse representations to identify underwater targets, since underwater acoustic signal have sparse characteristics. We consider the identification problem as one of the identifying among multiple linear regression models and believe that the new theory from sparse signal representation provides the key to solving this problem. Based on a sparse representation computed by 1 − minimization, we propose a general classification algorithm for (hydroacoustic signal-based) targets identification. This new framework provides new insights into identifying two key issues in underwater targets: feature extraction and robustness of signal loss and noise interference. For feature extraction, we point out that feature extraction is no longer critical if the sparseness of the underwater acoustic signal is properly utilized. The critical is whether the number of features is large enough and whether the sparse representation is correctly computed. This framework can handle errors due to signal loss and noise interference uniformly by exploiting the fact that these errors are often sparse with respect to the standard (hydroacoustic signal) basis. Extensive experiments have been conducted based on a public underwater acoustic signal sampling set to verify the efficacy of the proposed algorithm and corroborate the above claims. INDEX TERMS Identification of underwater targets, sparse representation, 1 − norm, compressed sensing.

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“…The notion of compressive robust subspace clustering, which is to perform robust subspace clustering with compressed data, has recently been proposed by Liu, Zhang, Liu, and Xiong (2019b). Compressive robust subspace clustering is generated by projecting the original high-dimensional data onto a lower-dimensional subspace chosen at random.…”

Section: Review Of Clustering In Machine Learningmentioning

confidence: 99%

Gradient-based Learning Methods Extended to Smooth Manifolds Applied to Automated Clustering

Koudounas¹,

Fiori

2020

jair

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Grassmann manifold based sparse spectral clustering is a classification technique that consists in learning a latent representation of data, formed by a subspace basis, which is sparse. In order to learn a latent representation, spectral clustering is formulated in terms of a loss minimization problem over a smooth manifold known as Grassmannian. Such minimization problem cannot be tackled by one of traditional gradient-based learning algorithms, which are only suitable to perform optimization in absence of constraints among parameters. It is, therefore, necessary to develop specific optimization/learning algorithms that are able to look for a local minimum of a loss function under smooth constraints in an efficient way. Such need calls for manifold optimization methods. In this paper, we extend classical gradient-based learning algorithms on at parameter spaces (from classical gradient descent to adaptive momentum) to curved spaces (smooth manifolds) by means of tools from manifold calculus. We compare clustering performances of these methods and known methods from the scientific literature. The obtained results confirm that the proposed learning algorithms prove lighter in computational complexity than existing ones without detriment in clustering efficacy.

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Robust Subspace Clustering With Compressed Data

Cited by 56 publications

References 40 publications

Classification of Remote Sensing Images Through Reweighted Sparse Subspace Representation Using Compressed Data

Classification of Remote Sensing Images Through Reweighted Sparse Subspace Representation Using Compressed Data

Identification of Underwater Targets Based on Sparse Representation

Gradient-based Learning Methods Extended to Smooth Manifolds Applied to Automated Clustering

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