Projects that are tagged with sparsity.


Logo Sparse PCA 2.0

by tbuehler - December 31, 2015, 16:24:42 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5386 views, 1293 downloads, 3 subscriptions

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

Changes:
  • 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

Logo SALSA.jl 0.0.5

by jumutc - September 28, 2015, 17:28:56 CET [ Project Homepage BibTeX Download ] 1199 views, 240 downloads, 1 subscription

About: SALSA (Software lab for Advanced machine Learning with Stochastic Algorithms) is an implementation of the well-known stochastic algorithms for Machine Learning developed in the high-level technical computing language Julia. The SALSA software package is designed to address challenges in sparse linear modelling, linear and non-linear Support Vector Machines applied to large data samples with user-centric and user-friendly emphasis.

Changes:

Initial Announcement on mloss.org.


Logo Sparse Compositional Metric Learning v1.1

by bellet - August 16, 2015, 16:41:20 CET [ BibTeX BibTeX for corresponding Paper Download ] 3458 views, 1212 downloads, 2 subscriptions

About: Scalable learning of global, multi-task and local metrics from data

Changes:

Various minor bug fixes and improvements. The basis and triplet generation now fully supports with datasets with very small classes and arbitrary labels (no need to be consecutive or positive). The computational and memory efficiency of the code when data is high dimensional has been largely improved, and we generate a rectangular (smaller) projection matrix when the number of selected basis is smaller than the dimension. K-NN classification with local metrics has been optimized and made significantly less costly in both time and memory.


Logo RankSVM NC 1.0

by rflamary - July 10, 2014, 15:51:21 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3245 views, 851 downloads, 1 subscription

About: This package is an implementation of a linear RankSVM solver with non-convex regularization.

Changes:

Initial Announcement on mloss.org.


Logo hapFabia 1.4.2

by hochreit - December 28, 2013, 17:24:29 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5417 views, 1076 downloads, 1 subscription

About: hapFabia is an R package for identification of very short segments of identity by descent (IBD) characterized by rare variants in large sequencing data. It detects 100 times smaller segments than previous methods.

Changes:

o citation update

o plot function improved


Logo SMIDAS 1.1

by ambujtewari - August 15, 2010, 18:51:51 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8838 views, 1831 downloads, 1 subscription

About: A stochastic variant of the mirror descent algorithm employing Langford and Zhang's truncated gradient idea to minimize L1 regularized loss minimization problems for classification and regression.

Changes:

Fixed major bug in implementation. The components of the iterate where the current example vector is zero were not being updated correctly. Thanks to Jonathan Chang for pointing out the error to us.


Logo RLS2 MATLAB Toolbox 0.7

by posaune - March 31, 2010, 20:37:11 CET [ Project Homepage BibTeX Download ] 9316 views, 1984 downloads, 1 subscription

About: RLS2 is an instance of multiple kernel learning algorithm to simultaneously learn a regularized predictor and the kernel function. RLS2LIN is a version of RLS2 specialized to linear kernels on each feature. The package contains a set of scripts that implements RLS2 and RLS2LIN, together with a Graphic User Interface to load data, perform training, validation, and plot results.

Changes:
  • New kernel functions (rbfall, rbfsingle, polyall, polysingle)
  • Improved interface for pre-processing operations
  • The interface now allows to disable bias
  • Fixed bugs in parameter passing (thanks to Andrea Schirru)