Robust canonical correlation analysis based on -norm minimization for feature learning and image recognition
S Wang, H Du, G Zhang, J Lu… - Journal of Electronic …, 2020 - spiedigitallibrary.org
Canonical correlation analysis (CCA) is a popular method that has been extensively used in
feature learning. In nature, the objective function of CCA is equivalent to minimizing the
distance of the paired data, and L 2-norm is used as the distance metric. We know that L 2-
norm-based objective function will emphasize the large distance pairs and de-emphasizes
the small distance pairs. To alleviate the aforementioned problems of CCA, we propose an
approach named CCA based on L 1-norm minimization (CCA-L1) for feature learning. To …
feature learning. In nature, the objective function of CCA is equivalent to minimizing the
distance of the paired data, and L 2-norm is used as the distance metric. We know that L 2-
norm-based objective function will emphasize the large distance pairs and de-emphasizes
the small distance pairs. To alleviate the aforementioned problems of CCA, we propose an
approach named CCA based on L 1-norm minimization (CCA-L1) for feature learning. To …
Robust canonical correlation analysis based on L1-norm minimization for feature learning and image recognition (Erratum).
S Wang, H Du, G Zhang, J Lu, J Yang - J. Electronic Imaging, 2020 - scholar.archive.org
Robust canonical correlation analysis based on L -norm minimization for feature learning
and image recognition (Erratum) … Robust canonical correlation analysis based on L 1 -norm
minimization for feature learning and image recognition (Erratum) … Robust canonical
correlation analysis based on L 1 -norm minimization for feature learning and image
recognition (Erratum) …
and image recognition (Erratum) … Robust canonical correlation analysis based on L 1 -norm
minimization for feature learning and image recognition (Erratum) … Robust canonical
correlation analysis based on L 1 -norm minimization for feature learning and image
recognition (Erratum) …
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