Research map / Learning theory and algorithms / Matrix and tensor methods
A scaled Bregman theorem with applications
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DN-pLMS instead of p-LMS | Adaptive filtering · Noisy signal processing | prediction error: exceeding 40% | Mixed |
| Initialize spherical k-means with GKM seeding instead of using SKM alone | SKM · Clustering on the sphere | k-means potential: almost divide the k-means potential by a factor 2 on some runs | Helped |
| Use GKM seeding instead of Forgy initialization | Spherical clustering · Clustering on the sphere | k-means potential: by at least a factor 2 | Helped |
| Use GKM seeding rather than waiting for converged SKM | Spherical clustering · Clustering on the sphere | k-means potential | Mixed |
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