Research map / Learning theory and algorithms / Matrix and tensor methods
Faster Binary Embeddings for Preserving Euclidean Distances
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use second-order instead of first-order ΣΔ quantization (ablation) | Method 2 (sparse JL + ΣΔ) · Yelp, ImageNet, Flickr30k, CIFAR-10 | MAPE: around 0.08 -> around 0.07 | Helped |
| Use higher-order stable ΣΔ quantization (r>1) instead of first-order greedy quantization (ablation) | Binary distance embedding · Yelp | mean squared relative error | Helped |
| Increase embedding dimension m while keeping p=64 (ablation) | Binary distance embedding · Yelp | mean squared relative error: m=256,512,1024,2048,4096 | Helped |
| Use r=3 with empirically optimized p(m) (ablation) | Binary distance embedding · Yelp | distance reconstruction error | Helped |
| Use sparse JL projections instead of FJLT projections (ablation) | ΣΔ binary distance embedding · Yelp | MAPE | No clear effect |
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