Information-theoretic Metric Learning README File
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1. Quick-start
To test the code, we have provided a demo Matlab script Test.m, which runs
metric learning with cross-validation and k-nearest neighbors over the
example data set Iris. Simply type 'Test' in the Matlab command prompt. If
everything is working properly, the accuracy of the nearest neighbor
classifier using the learned metric should be around 95%.
2. Using the code
For fully supervised cases, metric learning is generally invoked after
creating constraints from the labels---similarity constraints are generated
for pairs of points in the same class, and dissimilarity constraints are
generated for pairs of points in different classes. The function
MetricLearnAutotuneKnn.m creates random constraints from a labeled data set,
runs metric learning while varying the gamma parameter, and returns a
Mahalanobis matrix A. When a nearest neighbor classifier is desired, the
function CrossValidateKNN.m can be used to perform classification with
cross-validation.
For example, to generate cross-validated KNN accuracy on a data set X with
labels y using standard Euclidean distance, you can use the following
command:
acc=CrossValidateKNN(y,X,@(y,X) EuclideanDistance(X),num_folds,knn_neighbor_size);
The EuclideanDistance.m function is provided.
In many simple cases, the Test.m script can easily be modified to handle
other labeled data sets. In other cases, such as when there are
user-defined constraints, the ItmlAlg.m program can be invoked directly. As
input, this function requires the constraints, the data matrix, the initial
Mahalanobis matrix, and a set of parameters. See ItmlAlg.m for information
on the format for these inputs.
3. Citation
Finally, please acknowledge the use of our code with a citation:
Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, and Inderjit
S. Dhillon. "Information-theoretic Metric Learning." Proc. 24th
International Conference on Machine Learning (ICML), 2007.
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