ssvmKmeans

The implementation of structural SVM for clustering

• Coding Reference: PyStruct   superKmeans
• Paper Reference: Supervised K-means Clustering, Thomas Finley 2007

Requirements

cvxopt
numpy
scikit-learn
scipy

Learner

One-slack SSVM

Cutting-Plane Training of Structural SVMs

trainer = OneSlackSSVM(model, C=1, n_jobs=1, verbose=1, show_loss_every=1)

Functions

.fit(X, Y): X is iterable containing 2d training examples; Y is iterable containing 1d true labels

trainer.fit(X, Y)

.predict(X): X is one 2d example

y_pred = trainer.predict(X)

.scores(X, y): X is one 2d example; y is one 1d true labels. Return loss and discriminant function value

loss, discrim_fv = trainer.scores(X, y)

Model

Spectral

Compute a matrix's relaxed spectral clustering. This will return a matrix with the number of eigenvectors same as the number of clusters associated with the largest eigenvalues.

model = Spectral(n_clusters=3)

Point-Incremental K-means

model = Kmeans(n_clusters=3)

Discretized Spectral

It is a combination of the Spectral and Iterative Point-Incremental K-means.

model = DiscretizedSpectral(n_clusters=3)