What is TDA?
Topological Data Analysis (TDA) extracts structural insights from data through persistent homology, revealing multi-scale patterns that traditional ML methods often miss.
// Basic TDA pipeline
const topologicalFeatures = TDA.analyze(inputData)
Persistent Homology
By tracking how topological features persist across varying scales, we can identify significant structures that enhance model interpretability and performance.
Homology Visualization
Mapping persistent intervals to significant topological features.
See Analysis →Machine Learning Integration
Integrating topological features improves model robustness by filtering noisy data. This approach stabilizes training and improves generalization.
Feature Enhancement
Augmenting models with persistent diagrams for better decision boundaries.
View Enhancements →TDA Implementation
// Topological feature extraction
const features = TDA.getFeatures({
dataset: mnistData,
parameters: { resolution: 128 }
})
// Feature integration pipeline
model.add(features.ripser())