About: A fast and scalable graph-based clustering algorithm based on the eigenvectors of the nonlinear 1-Laplacian.

• improved optimization of ncut and rcut criterion
• optimized eigenvector initialization
• changed default values for number of runs
• several internal optimizations
• made console output more informative

About: A generalized version of spectral clustering using the graph p-Laplacian.

various internal optimizations

About: A Matlab implementation of Sparse PCA using the inverse power method for nonlinear eigenproblems.

• Added deflation scheme to compute multiple principal components
• Several internal runtime and memory optimizations
• API change: sparsePCA.m is now used to compute multiple components; use computeTradeOffCurve.m to reproduce the examples in the NIPS paper

About: A community detection method based on constrained fractional set programming (CFSP).

Initial Announcement on mloss.org.