Research map / Optimization / Distributed and compressed
Distributed Principal Component Analysis with Limited Communication
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Riemannian gradient quantization instead of naive Euclidean gradient quantization (ablation) | quantized distributed gradient descent · four real datasets, cost -x^T A x | cost | Helped |
| quantize power iteration instead of using full precision (ablation) | power iteration · four real datasets | cost | Hurt |
| use 4 bits per coordinate instead of full precision (ablation) | Riemannian gradient quantization · four real datasets | cost: 16x compression | No clear effect |
| reduce to 1-3 bits per coordinate (ablation) | quantized methods · four real datasets | convergence stability: 1-3 bits | Hurt |
| increase to 8-10 bits per coordinate (ablation) | quantized methods · four real datasets | cost: 8-10 bits | Helped |
Closest papers by meaning
- Communication-Efficient Distributed PCA by Riemannian Optimization (ICML 2020 · similarity 0.92)
- Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis (ICML 2017 · similarity 0.90)
- Distributed Low-rank Matrix Factorization With Exact Consensus (NeurIPS 2019 · similarity 0.87)
- A Comprehensively Tight Analysis of Gradient Descent for PCA (NeurIPS 2021 · similarity 0.86)
- Nonlinear Laplacians: Tunable principal component analysis under directional prior information (NeurIPS 2025 · similarity 0.85)
- Riemannian Diffusion Adaptation for Distributed Optimization on Manifolds (ICML 2025 · similarity 0.84)
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