Welcome to PySEF’s documentation!

PySEF provides an implementation of the Similarity Embedding Framework (SEF) on top of the PyTorch library. PySEF is an easy to use Python library that exposes a scikit-learn-like interface, allows for easily implementing novel dimensionality reduction techniques and can efficiently handle large-scale dataset using the GPU.

What is the Similarity Embedding Framework?

The vast majority of Dimensionality Reduction techniques rely on second-order statistics to define their optimization objective. Even though this provides adequate results in most cases, it comes with several shortcomings. The methods require carefully designed regularizers and they are usually prone to outliers. The Similarity Embedding Framework can overcome the aforementioned limitations and provides a conceptually simpler way to express optimization targets similar to existing DR techniques. Deriving a new DR technique using the Similarity Embedding Framework becomes simply a matter of choosing an appropriate target similarity matrix. A variety of classical tasks, such as performing supervised dimensionality reduction and providing out-of-of-sample extensions, as well as, new novel techniques, such as providing fast linear embeddings for complex techniques, are demonstrated.

Contents:

• Installation
  • Recommended (pip-based) installation
  • Bleeding-edge installation
• Using PySEF
  • How to get started?
  • Using GPU acceleration
  • Data loading
  • Recreating the geometry of a high dimensional space into a space with less dimensions
  • Re-deriving similarity-based versions of well-known techniques
  • Providing out-of-sample extensions
  • Performing SVM-based analysis
• Examples and Tutorials
  • Prerequisites
  • Linear approximation of a high-dimensional technique
  • Supervised dimensionality reduction
  • Providing out-of-sample-extensions
  • SVM-based analysis
  • PySEF tutorials
• Extending PySEF
  • Deriving new DR methods