Group Factor Analysis (GFA)

Benchmarks for Group Factor Analysis covering runtime scaling and parameter recovery.

Runtime benchmarks validate theoretical complexity:

• Linear scaling in samples (N) and observed dimensions (D)

• Cubic scaling in latent dimensions (K), though optimized LAPACK routines yield quadratic scaling at typical sizes

Recovery benchmarks verify parameter estimation across:

• Sample sizes and noise levels

• Observed and latent dimensionalities

• Number of groups

Dimensionality recovery benchmarks evaluate post-hoc latent dimension selection across sample sizes and noise levels.

GFA runtime benchmarks

GFA runtime benchmarks

GFA parameter recovery benchmarks

GFA parameter recovery benchmarks

GFA dimensionality recovery benchmarks

GFA dimensionality recovery benchmarks