Computer Science > Machine Learning
[Submitted on 16 Jun 2014 (v1), last revised 2 Jun 2015 (this version, v2)]
Title:Multi-stage Multi-task feature learning via adaptive threshold
View PDFAbstract:Multi-task feature learning aims to identity the shared features among tasks to improve generalization. It has been shown that by minimizing non-convex learning models, a better solution than the convex alternatives can be obtained. Therefore, a non-convex model based on the capped-$\ell_{1},\ell_{1}$ regularization was proposed in \cite{Gong2013}, and a corresponding efficient multi-stage multi-task feature learning algorithm (MSMTFL) was presented. However, this algorithm harnesses a prescribed fixed threshold in the definition of the capped-$\ell_{1},\ell_{1}$ regularization and the lack of adaptivity might result in suboptimal performance. In this paper we propose to employ an adaptive threshold in the capped-$\ell_{1},\ell_{1}$ regularized formulation, where the corresponding variant of MSMTFL will incorporate an additional component to adaptively determine the threshold value. This variant is expected to achieve a better feature selection performance over the original MSMTFL algorithm. In particular, the embedded adaptive threshold component comes from our previously proposed iterative support detection (ISD) method \cite{Wang2010}. Empirical studies on both synthetic and real-world data sets demonstrate the effectiveness of this new variant over the original MSMTFL.
Submission history
From: Yilun Wang [view email][v1] Mon, 16 Jun 2014 12:47:37 UTC (260 KB)
[v2] Tue, 2 Jun 2015 19:47:37 UTC (341 KB)
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