Research map / Learning theory and algorithms / Matrix and tensor methods
Revisiting Metric Learning for SPD Matrix Based Visual Representation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply α-CML to Ker-RP-RBF before k-NN classification | Ker-RP-RBF · MSR-Action3D | accuracy: 88.1 -> 92.7 | Helped |
| Apply α-CML to Ker-RP-RBF before SVM classification | Ker-RP-RBF · MSR-Action3D | accuracy: 96.1 -> 97.3 | Helped |
| Apply α-CML to inverse covariance representation for k-NN | α-CML · ADHD-200 | accuracy: 64.3 -> 68.4 | Helped |
| Apply α-CML to inverse covariance representation for SVM | α-CML · ADHD-200 | accuracy: 66.7 -> 69.6 | Helped |
| Use α-CML rather than LEML | SPD metric learning · Brodatz | accuracy | Mixed |
| Add LEML after α-CML | α-CML · HDM05 | accuracy: 91.0 -> 96.6 | Helped |
| Add LEML after α-CML | α-CML · MSR-Action3D | accuracy: 92.7 -> 94.6 | Helped |
| Apply α-CML to covariance descriptors | Brodatz binary classification · Brodatz, 15 difficult pairs | accuracy improvement: 4.2 ± 5.5 | Helped |
| Apply α-CML to covariance descriptors | Brodatz multi-class classification · Brodatz, 112 classes | accuracy improvement: 2.5 ± 1.0 | Helped |
| Use α-CML rather than LEML | SPD metric learning · ADHD-200 | accuracy: 68.4 vs 67.3 | Helped |
Closest papers by meaning
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- Efficient Stochastic Optimization for Low-Rank Distance Metric Learning (AAAI 2017 · similarity 0.85)
- Fixed-Rank Supervised Metric Learning on Riemannian Manifold (AAAI 2016 · similarity 0.84)
- Approximate Log-Hilbert-Schmidt Distances Between Covariance Operators for Image Classification (CVPR 2016 · similarity 0.84)
- Matrix Variate Gaussian Mixture Distribution Steered Robust Metric Learning (AAAI 2018 · similarity 0.84)
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