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Global Search and Analysis for the Nonconvex Two-Level ℓ₁ Penalty

Fan He, Mingzhen He, Lei Shi, Xiaolin Huang

in TNNLS, 2022

This work presents the nonconvex two-level l1 penalty based on tis piecewise linear property and apply it to machine learning tasks.

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Learning with Asymmetric Kernels: Least Squares and Feature Interpretation

Mingzhen He, Fan He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens

in TPAMI, 2023

This paper addresses the asymmetric kernel-based learning in the framework of the least squares support vector machine, resulting in the first classification method that can utilize asymmetric kernels directly.

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Learning non-parametric kernels via matrix decomposition for logistic regression

Kaijie Wang, Fan He, Mingzhen He, Xiaolin Huang

in Pattern Recognition Letters, 2023

This work presents several low rank decompostion schemes to adjust the kernel Gram matrices, which improves the flexibility of kernels without using conplicated rank penalty.

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Diffusion representation for asymmetric kernels via magnetic transform

Mingzhen He, Fan He, Ruikai Yang, Xiaolin Huang

in NeurIPS, 2024

This method maps an asymmetric kernel to the complex-valued field, embedding the symmetric and skew-symmetric parts to module and phase respectively, which enables a new dimension reduction method for data endowed with an asymmetric similarity such as directed graphs and trophic networks.

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Decentralized Kernel Ridge Regression Based on Data-Dependent Random Feature

Ruikai Yang, Fan He, Mingzhen He, Jie Yang, Xiaolin Huang

in TNNLS, 2024

This work proposes a new decentralized KRR algorithm that pursues consensus on decision functions, which allows great flexibility and well adapts data on nodes.

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