Projects that are tagged with sparse.


Logo JMLR MLPACK 2.0.2

by rcurtin - June 20, 2016, 22:23:45 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 59782 views, 10967 downloads, 6 subscriptions

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About: A scalable, fast C++ machine learning library, with emphasis on usability.

Changes:
  • Added the function LSHSearch::Projections(), which returns an arma::cube with each projection table in a slice (#663). Instead of Projection(i), you should now use Projections().slice(i).
  • A new constructor has been added to LSHSearch that creates objects using projection tables provided in an arma::cube (#663).
  • LSHSearch projection tables refactored for speed (#675).
  • Handle zero-variance dimensions in DET (#515).
  • Add MiniBatchSGD optimizer (src/mlpack/core/optimizers/minibatch_sgd/) and allow its use in mlpack_logistic_regression and mlpack_nca programs.
  • Add better backtrace support from Grzegorz Krajewski for Log::Fatal messages when compiled with debugging and profiling symbols. This requires libbfd and libdl to be present during compilation.
  • CosineTree test fix from Mikhail Lozhnikov (#358).
  • Fixed HMM initial state estimation (#600).
  • Changed versioning macros _MLPACKVERSION_MAJOR, _MLPACKVERSION_MINOR, and _MLPACKVERSION_PATCH to MLPACK_VERSION_MAJOR, MLPACK_VERSION_MINOR, and MLPACK_VERSION_PATCH. The old names will remain in place until mlpack 3.0.0.
  • Renamed mlpack_allknn, mlpack_allkfn, and mlpack_allkrann to mlpack_knn, mlpack_kfn, and mlpack_krann. The mlpack_allknn, mlpack_allkfn, and mlpack_allkrann programs will remain as copies until mlpack 3.0.0.
  • Add --random_initialization option to mlpack_hmm_train, for use when no labels are provided.
  • Add --kill_empty_clusters option to mlpack_kmeans and KillEmptyClusters policy for the KMeans class (#595, #596).

Logo Sparse PCA 2.0

by tbuehler - December 31, 2015, 16:24:42 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5595 views, 1335 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 Probabilistic Classification Vector Machine 0.22

by fmschleif - November 10, 2015, 13:16:19 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5028 views, 1112 downloads, 3 subscriptions

About: PCVM library a c++/armadillo implementation of the Probabilistic Classification Vector Machine.

Changes:

30.10.2015 * code has been revised in some places fixing also some errors different multiclass schemes and hdf5 file support added. Some speed ups and memory savings by better handling of intermediate objects.

27.05.2015: - Matlab binding under Windows available. Added a solution file for VS'2013 express to compile a matlab mex binding. Can not yet confirm that under windows the code is really using multiple cores (under linux it does)

29.04.2015 * added an implementation of the Nystroem based PCVM includes: Nystroem based singular value decomposition (SVD), eigenvalue decomposition (EVD) and pseudo-inverse calculation (PINV)

22.04.2015 * implementation of the PCVM released


Logo sccan 0.0

by stnava - January 13, 2011, 18:14:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4816 views, 1218 downloads, 1 subscription

About: A work in progress

Changes:

Initial Announcement on mloss.org.


Logo redsvd 0.1.0

by hillbig - August 30, 2010, 18:13:55 CET [ Project Homepage BibTeX Download ] 5418 views, 1179 downloads, 1 subscription

About: redsvd is a library for solving several matrix decomposition (SVD, PCA, eigen value decomposition) redsvd can handle very large matrix efficiently, and optimized for a truncated SVD of sparse matrices. For example, redsvd can compute a truncated SVD with top 20 singular values for a 100K x 100K matrix with 10M nonzero entries in about two second.

Changes:

Initial Announcement on mloss.org.