Metric Learning algorithms in Python.
Algorithms
- Large Margin Nearest Neighbor (LMNN)
- Information Theoretic Metric Learning (ITML)
- Sparse Determinant Metric Learning (SDML)
- Least Squares Metric Learning (LSML)
Dependencies
- Python 2.6+
- numpy, scipy, scikit-learn
- (for the demo only: matplotlib)
Usage
For full usage examples, see test.py and demo.py.
Each metric is a subclass of BaseMetricLearner, which provides default implementations for the methods
metric, transformer, and transform.
Subclasses must provide an implementation for either metric or transformer.
For an instance of a metric learner named foo learning from a set of d-dimensional points,
foo.metric() returns a d by d matrix M such that a distance between vectors x and y is
expressed (x-y).dot(M).dot(x-y).
In the same scenario, foo.transformer() returns a d by d matrix L such that a vector x
can be represented in the learned space as the vector L.dot(x).
For convenience, the function foo.transform(X) is provided for converting a matrix of points (X)
into the learned space, in which standard Euclidean distance can be used.
Notes
If a recent version of the Shogun Python modular (modshogun) library is available,
the LMNN implementation will use the fast C++ version from there.
The two implementations differ slightly, and the C++ version is more complete.