ℓ1,∞-Mixed Norm Promoted Row Sparsity for Fast Online CUR Decomposition Learning in Varying Feature Spaces
Conference
Chen, Z, He, Y, Wu, D et al. (2025). ℓ1,∞-Mixed Norm Promoted Row Sparsity for Fast Online CUR Decomposition Learning in Varying Feature Spaces
. 124-133. 10.1137/1.9781611978520.11
Chen, Z, He, Y, Wu, D et al. (2025). ℓ1,∞-Mixed Norm Promoted Row Sparsity for Fast Online CUR Decomposition Learning in Varying Feature Spaces
. 124-133. 10.1137/1.9781611978520.11
Online learning enables effective predictive modeling on complex data streams. To overcome the negative impact of possibly high-dimensional data, sparse online learning (SOL) has been proposed by imposing various sparse constraints to sheer the resultant model structure. However, most existing SOL studies focused on a fixed feature space, whereas in practice the steaming data observations may increment in both quantity and feature dimensions, leading to varying feature spaces. In this paper, we propose a novel ℓ1,∞-mixed norm-based row sparsity SOL algorithm (SOOFS) to handle data streams in varying feature spaces. We empower SOOFS with a tailored online CUR matrix decomposition method based on the promoted row sparsity to actively and adaptively select informative instances in the sliding windows, facilitating stable online performance over time. Empirical results on ten benchmark datasets substantiate the superiority of SOOFS over three state-of-the-art competitors in terms of classification accuracy and model sparsity.
