Research map / Probabilistic and scientific modeling / Molecules and proteins
Additive Component Analysis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace linear PCA components with smooth additive nonlinear basis functions | ACA · Synthetic manifold with Gaussian noise and outliers | robustness to noise | Helped |
| Use explicit nonlinear reconstruction with ACA instead of PCA | Face image reconstruction · Extended Yale Face Database B | reconstruction quality | Helped |
| Use deep ACA with two jointly learned layers instead of one ACA layer (ablation) | ACA · Translated-circle images | reconstruction performance | Helped |
| Jointly learn two ACA layers instead of learning them greedily layer by layer (ablation) | Two-layer ACA · Translated-circle images | latent representation interpretability | Helped |
| Increase ACA roughness parameter ρ (ablation) | Deep ACA · COIL-20 test images | class separability | Mixed |
| Use approximate stochastic optimization instead of batch optimization (ablation) | ACA · MNIST | training convergence speed: less than 10 minutes | Helped |
| Use batch optimization instead of approximate stochastic optimization (ablation) | ACA · MNIST | training reconstruction error | Helped |
| Use approximate stochastic optimization instead of batch optimization (ablation) | ACA · MNIST | generalization | Helped |
Closest papers by meaning
- Efficient Sparse PCA via Block-Diagonalization (ICLR 2025 · similarity 0.87)
- Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis (NeurIPS 2024 · similarity 0.87)
- Rethinking PCA Through Duality (NeurIPS 2025 · similarity 0.87)
- Capturing the denoising effect of PCA via compression ratio (NeurIPS 2024 · similarity 0.86)
- Feature learning from non-Gaussian inputs: the case of Independent Component Analysis in high dimensions (ICML 2025 · similarity 0.86)
- Principle Component Trees and Their Persistent Homology (AAAI 2024 · similarity 0.86)
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