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Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding
2015
Pattern Recognition
In computer vision and pattern recognition researches, the studied objects are often characterized by multiple feature representations with high dimensionality, thus it is essential to encode that multiview feature into a unified and discriminative embedding that is optimal for a given task. To address this challenge, this paper proposes an ensemble manifold regularized sparse low-rank approximation (EMR-SLRA) algorithm for multiview feature embedding. The EMR-SLRA algorithm is based on the
doi:10.1016/j.patcog.2014.12.016
fatcat:lpv6nr5jmjb5ngl6lmlaas43me