Hyperspace Analysis in XAI

Hyperspace analysis extends beyond traditional dimensionality reduction by exploring the complete high-dimensional space where AI models operate. This approach reveals complex patterns, clusters, and decision boundaries that might be lost in simpler projections.

Hyperspace Visualization

Core Components

Dimensionality Analysis

Understand how your model navigates high-dimensional spaces and identify the most relevant dimensions for decision-making. This helps in feature selection and model optimization.

Dimensions Feature Space Complexity

Manifold Learning

Discover the underlying structure of your data using advanced manifold learning techniques. This reveals natural clusters and patterns that inform model behavior.

Topology Structure Patterns

Distance Metrics

Explore different distance metrics in hyperspace to understand how your model measures similarity between data points. This is crucial for clustering and nearest neighbor analyses.

Metrics Similarity Distance

Implementation Example

Here's a simple example of hyperspace analysis implementation:

import numpy as np
from sklearn.manifold import MDS
from sklearn.metrics import pairwise_distances

def analyze_hyperspace(X, metric='euclidean'):
    # Calculate pairwise distances
    distances = pairwise_distances(X, metric=metric)
    
    # Perform multidimensional scaling
    mds = MDS(n_components=3, dissimilarity='precomputed')
    X_transformed = mds.fit_transform(distances)
    
    # Analyze local structure
    return analyze_local_structure(X_transformed)

Best Practices

  • Consider multiple distance metrics for robust analysis
  • Use dimensionality reduction techniques wisely
  • Validate findings across different subspaces
  • Combine global and local analysis methods
  • Account for the curse of dimensionality