Dynamic Graph Topology Learning with Non-Convex Penalties

Mirzaeifard, Reza; Gogineni, Vinay Chakravarthi; Venkategowda, Naveen K. D.; Werner, Stefan

doi:10.23919/eusipco55093.2022.9909609

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…One solution to this problem is using sparse penalties, such as the minimax concave penalty (MCP) [13] and the smoothly clipped absolute deviation (SCAD) [14], that are capable of intelligently distinguishing between active and inactive coefficients. Although these penalties encourage sparse solutions, they mitigate the bias effect of the l 1 -penalty [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Distributed Quantile Regression with Non-Convex Sparse Penalties

Mirzaeifard,

Gogineni,

Venkategowda

et al. 2023

2023 IEEE Statistical Signal Processing Workshop (SSP)

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The surge in data generated by IoT sensors has increased the need for scalable and efficient data analysis methods, particularly for robust algorithms like quantile regression, which can be tailored to meet a variety of situations, including nonlinear relationships, distributions with heavy tails, and outliers. This paper presents a sub-gradient-based algorithm for distributed quantile regression with non-convex, and non-smooth sparse penalties such as the Minimax Concave Penalty (MCP) and Smoothly Clipped Absolute Deviation (SCAD). These penalties selectively shrink non-active coefficients towards zero, addressing the limitations of traditional penalties like the l1-penalty in sparse models. Existing quantile regression algorithms with nonconvex penalties are designed for centralized cases, whereas our proposed method can be applied to distributed quantile regression using non-convex penalties, thereby improving estimation accuracy. We provide a convergence proof for our proposed algorithm and demonstrate through numerical simulations that it outperforms state-of-the-art algorithms in sparse and moderately sparse scenarios.

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Section: Introductionmentioning

confidence: 99%

Distributed Quantile Regression with Non-Convex Sparse Penalties

Mirzaeifard,

Gogineni,

Venkategowda

et al. 2023

2023 IEEE Statistical Signal Processing Workshop (SSP)

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“…While non-convex and non-smooth penalties may improve estimation accuracy in many problems [24,25], because of their non-convexity and non-smoothness, they complicate optimization. For penalized robust penalized phase retrieval, in particular, proposing an optimization algorithm is more challenging due to its non-convex and non-smooth nature.…”

Section: Introductionmentioning

confidence: 99%

Robust Phase Retrieval with Non-Convex Penalties

Mirzaeifard

Venkategowda

Werner

2022

2022 56th Asilomar Conference on Signals, Systems, and Computers

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This paper proposes an alternating direction method of multiplier (ADMM) based algorithm for solving the sparse robust phase retrieval with non-convex and non-smooth sparse penalties, such as minimax concave penalty (MCP). The accuracy of the robust phase retrieval, which employs an l1 based estimator to handle outliers, can be improved in a sparse situation by adding a non-convex and non-smooth penalty function, such as MCP, which can provide sparsity with a low bias effect. This problem can be effectively solved using a novel proximal ADMM algorithm, and under mild conditions, the algorithm is shown to converge to a stationary point. Several simulation results are presented to verify the accuracy and efficiency of the proposed approach compared to existing methods.

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Dynamic Graph Topology Learning with Non-Convex Penalties

Cited by 2 publications

References 17 publications

Distributed Quantile Regression with Non-Convex Sparse Penalties

Distributed Quantile Regression with Non-Convex Sparse Penalties

Robust Phase Retrieval with Non-Convex Penalties

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