Dimensionality Reduction
The Dimensionality Reduction module in ChemXploreML provides powerful tools for reducing the complexity of molecular data while preserving important structural and property information.
Overview
The Dimensionality Reduction interface offers:
Multiple Algorithms
- Principal Component Analysis (PCA)
- t-SNE
- UMAP
- Other advanced techniques
Visualization Tools
- 2D/3D scatter plots
- Interactive visualizations
- Cluster highlighting
- Property mapping
PCA Implementation
Principal Component Analysis (PCA) is one of the most commonly used dimensionality reduction techniques in ChemXploreML:
Features
- Automatic component selection
- Variance explained analysis
- Component contribution analysis
- Interactive visualization
Usage
- Select PCA from the algorithm options
- Configure parameters:
- Number of components
- Scaling options
- Feature selection
- Run the analysis
- Explore results
Key Features
Algorithm Selection
- Choose from multiple algorithms
- Compare different methods
- Optimize parameters
- Save configurations
Visualization
- Interactive 2D/3D plots
- Property mapping
- Cluster highlighting
- Export options
Analysis Tools
- Variance analysis
- Component contribution
- Cluster analysis
- Outlier detection
Best Practices
Data Preparation
- Standardize features
- Handle missing values
- Remove outliers
- Select relevant features
Algorithm Selection
- Consider data size
- Account for computational resources
- Match algorithm to goals
- Validate results
Parameter Tuning
- Optimize number of components
- Adjust algorithm parameters
- Validate results
- Document settings
Next Steps
After performing dimensionality reduction, you can:
- Use the reduced data for ML Training
- Generate Molecular Embeddings
- Perform Molecular Analysis
For more detailed information about specific algorithms or visualization options, please refer to the respective documentation sections.