Abstract
Recovering missing values from incomplete traffic sensor data is an important task for intelligent transportation system because most algorithms require data with complete entries as input. Self-representation-based matrix completion attempts to optimally represent each sample by linearly combining other samples when conducting missing values recovery. Typically, it implements sparse or dense combination through imposing either $l_{1}$ -norm or $l_{2}$ -norm regularization over the representation coefficients, which is not always optimal in practice. To permit more flexibility, we propose in this paper a novel approach termed as $l_{p}$ -norm regularized sparse self-representation (SSR- $l_{p}$ ) by incorporating nonconvex $l_{p}$ -norm with $0 as regularization. In such a way, it is able to produce more sparsity than $l_{1}$ -norm and in turn facilitates the accurate recovery of missing data. We further develop an efficient iterative algorithm for solving SSR- $l_{p}$ . The performance of this method is evaluated on a real-world road network traffic flow data set. The experimental results verify the advantage of our method over other competing algorithms in recovering missing values.
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