Multi-level network Lasso for multi-task personalized learning

doi:10.1016/j.patcog.2024.111213

Multi-level network Lasso for multi-task personalized learning

2025-05-01

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DOI：10.1016/j.patcog.2024.111213

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We propose the multi-level network Lasso, which aims to overcome the key limitations of existing personalized learning methods, such as ignoring sample homogeneity or heterogeneity, and over-parametrization. Multi-level network Lasso learns both sample-common model and sample-specific model, that are succinct and interpretable in the sense that model parameters are shared across neighboring samples based on only a subset of relevant features. To apply personalized learning in multi-task scenarios, we further extend the multi-level network Lasso for multi-task personalized learning by learning underlying task groups in the feature subspace. Additionally, we investigate a family of the multi-level network Lasso based on the & ell;(p) quasi-norm (0 <1), that helps prevent over-penalization on large group outliers. An alternating algorithm is developed to efficiently solve the proposed optimization problem. Experimental results on synthetic and real-world datasets demonstrate the effectiveness of the proposed method.

Keywords:

Multi-level network Lasso

Personalized learning

Multi-task learning

Multi-level network Lasso for multi-task personalized learning

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