Low-Rank Templates
Cross-source consensus on Low-Rank Templates from 1 sources and 4 claims.
1 sources · 4 claims
How it works
Benefits
Highlighted claims
- Each template is parameterized by an orthonormal factor matrix, signed spectral weights, and a global scale. — A Bayesian Adaptive Latent Mixture Model for Zero-Inflated Weighted Brain Connectome Analysis
- Positive and negative spectral weights allow assortative and disassortative patterns. — A Bayesian Adaptive Latent Mixture Model for Zero-Inflated Weighted Brain Connectome Analysis
- Inference focuses on reconstructed off-diagonal templates because only off-diagonal edges enter the likelihood. — A Bayesian Adaptive Latent Mixture Model for Zero-Inflated Weighted Brain Connectome Analysis
- Low-rank score templates allow diffuse motifs rather than rigid block-constant communities. — A Bayesian Adaptive Latent Mixture Model for Zero-Inflated Weighted Brain Connectome Analysis