Circuit Design and Analysis of Smoothed $${l}_0$$ Norm Approximation for Sparse Signal Reconstruction

Li, Jianjun; Che, Hangjun; Liu, Xiaoyang

doi:10.1007/s00034-022-02216-y

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…For the proposed method, λ is tuned in the range of [1,5,10,15,20,50, 100, 500, 1000, 2000, 3000, 5000], α is tuned in the range of [1,5,10,50, 100, 500, 1000], θ is tuned in the range of [0.5, 1, 1.2, 1.5, 2, 2.5, 3, 3.5, 4] and the scaling parameter γ is fixed as 0.2.…”

Section: Parameter Settingsmentioning

confidence: 99%

“…There are four hyperparameters in our method, i.e., two balance parameters λ and α, a nonconvex function parameter θ and a scale parameter γ. The parameters are analyzed on six datasets, λ is tuned from [5,10,15,20,50, 100, 500, 1000, 5000], α is tuned from [1, 5, 10, 50, 100, 500, 1000, 5000], the range of θ is [0.5, 1, 2, 3, 4] and γ is chosen from [0.001, 0.01, 0.1, 0.2, 0.25]. We only display the ACC values for different combinations of parameters.…”

Section: Parameter Sensitivity and Time Complexity Analysismentioning

confidence: 99%

“…(2) Inspired by [18][19][20], a novel norm named the Cauchy norm with an upper bound is proposed to handle the noise in the tensor. Compared with the widely used l 2,1 norm, the Cauchy norm is more stable, due to its asymptotic upper bound, which alleviates the influence of noise, especially in the case of high-noise values.…”

Section: Introductionmentioning

confidence: 99%

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Graph-based multi-view clustering methods aim to explore the partition patterns by utilizing a similarity graph. However, many existing methods construct a consensus similarity graph based on the original multi-view space, which may result in the lack of information on the underlying low-dimensional space. Additionally, these methods often fail to effectively handle the noise present in the graph. To address these issues, a novel graph-based multi-view clustering method which combines spectral embedding, non-convex low-rank approximation and noise processing into a unit framework is proposed. In detail, the proposed method constructs a tensor by stacking the inner product of normalized spectral embedding matrices obtained from each similarity matrix. Then, the obtained tensor is decomposed into a low-rank tensor and a noise tensor. The low-rank tensor is constrained via nonconvex low-rank tensor approximation and a novel Cauchy norm with an upper bound is proposed to handle the noise. Finally, we derive the consensus similarity graph from the denoised low-rank tensor. The experiments on five datasets demonstrate that the proposed method outperforms other state-of-the-art methods on five datasets.

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Section: Parameter Settingsmentioning

confidence: 99%

Section: Parameter Sensitivity and Time Complexity Analysismentioning

confidence: 99%

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An Intelligent System for Trading Signal of Cryptocurrency Based on Market Tweets Sentiments

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Circuit Design and Analysis of Smoothed $${l}_0$$ Norm Approximation for Sparse Signal Reconstruction

Cited by 4 publications

References 29 publications

Robust Low-Rank Graph Multi-View Clustering via Cauchy Norm Minimization

Robust Low-Rank Graph Multi-View Clustering via Cauchy Norm Minimization

An Adaptive Method Based on Smoothed Approximation for Sparse Signal Reconstruction

An Intelligent System for Trading Signal of Cryptocurrency Based on Market Tweets Sentiments

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