Removed overclustering support from k-means because it is not well-tested, may be buggy, and is (I think) unused. If this was support you were using, open a bug or get in touch with us; it would not be hard for us to reimplement it.

Refactored KMeans to allow different types of Lloyd iterations.

Added implementations of k-means: Elkan's algorithm, Hamerly's algorithm, Pelleg-Moore's algorithm, and the DTNN (dual-tree nearest neighbor) algorithm.

Significant acceleration of LRSDP via the use of accu(a % b) instead of trace(a * b).

Added MatrixCompletion class (matrix_completion), which performs nuclear norm minimization to fill unknown values of an input matrix.

Proper handling of dimension calculation in PCA.

Load parameter vectors properly for LinearRegression models.

Linker fixes for AugLagrangian specializations under Visual Studio.

Add support for observation weights to LinearRegression.

MahalanobisDistance<> now takes root of the distance by default and therefore satisfies the triangle inequality (TakeRoot now defaults to true).

Better handling of optional Armadillo HDF5 dependency.

Fixes for numerous intermittent test failures.

math::RandomSeed() now sets the random seed for recent (>=3.930) Armadillo versions.

Handle Newton method convergence better for SparseCoding::OptimizeDictionary() and make maximum iterations a parameter.

Known bug: CosineTree construction may fail in some cases on i386 systems. (#376)

Bugfix for NeighborSearch regression which caused very slow allknn/allkfn. Speeds are now restored to approximately 1.0.8 speeds, with significant improvement for the cover tree.

Detect dependencies correctly when ARMA_USE_WRAPPER is not being defined (i.e., libarmadillo.so does not exist).

Bugfix for compilation under Visual Studio.

GMM initialization is now safer and provides a working GMM when constructed with only the dimensionality and number of Gaussians (#314).

Check for division by 0 in Forward-Backward Algorithm in HMMs (#314).

Fix MaxVarianceNewCluster (used when re-initializing clusters for k-means) (#314).

Fixed implementation of Viterbi algorithm in HMM::Predict() (#316).

Significant speedups for dual-tree algorithms using the cover tree (#243, #329) including a faster implementation of FastMKS.

Fix for LRSDP optimizer so that it compiles and can be used (#325).

CF (collaborative filtering) now expects users and items to be zero-indexed, not one-indexed (#324).

CF::GetRecommendations() API change: now requires the number of recommendations as the first parameter. The number of users in the local neighborhood should be specified with CF::NumUsersForSimilarity().

Removed incorrect PeriodicHRectBound (#30).

Refactor LRSDP into LRSDP class and standalone function to be optimized (#318).

Fix for centering in kernel PCA (#355).

Added simulated annealing (SA) optimizer, contributed by Zhihao Lou.

HMMs now support initial state probabilities; these can be set in the constructor, trained, or set manually with HMM::Initial() (#315).

Added Nyström method for kernel matrix approximation by Marcus Edel.

Kernel PCA now supports using Nyström method for approximation.

Ball trees now work with dual-tree algorithms, via the BallBound<> bound structure (#320); fixed by Yash Vadalia.

The NMF class is now AMF<>, and supports far more types of factorizations, by Sumedh Ghaisas.

A QUIC-SVD implementation has returned, written by Siddharth Agrawal and based on older code from Mudit Gupta.

Added perceptron and decision stump by Udit Saxena (these are weak learners for an eventual AdaBoost class).

Sparse autoencoder added by Siddharth Agrawal.

Memory leak in NeighborSearch index-mapping code fixed (#310).

GMMs can be trained using the existing model as a starting point by specifying an additional boolean parameter to GMM::Estimate() (#308).

Logistic regression implementation added in methods/logistic_regression (see also #305).

L-BFGS optimizer now returns its function via Function().

Version information is now obtainable via mlpack::util::GetVersion() or the __MLPACK_VERSION_MAJOR, __MLPACK_VERSION_MINOR, and __MLPACK_VERSION_PATCH macros (#309).

Fix typos in allkfn and allkrann output.

Cover tree support for range search (range_search), rank-approximate nearest neighbors (allkrann), minimum spanning tree calculation (emst), and FastMKS (fastmks).

Dual-tree FastMKS implementation added and tested.

Added collaborative filtering package (cf) that can provide recommendations when given users and items.

Fix for correctness of Kernel PCA (kernel_pca) (#280).

Speedups for PCA and Kernel PCA (#204).

Fix for correctness of Neighborhood Components Analysis (NCA) (#289).

Minor speedups for dual-tree algorithms.

Fix for Naive Bayes Classifier (nbc) (#279).

Added a ridge regression option to LinearRegression (linear_regression) (#298).

Gaussian Mixture Models (gmm::GMM<>) now support arbitrary covariance matrix constraints (#294).

MVU (mvu) removed because it is known to not work (#189).

Minor updates and fixes for kernels (in mlpack::kernel).

Speedups of cover tree traversers (#243).

Addition of rank-approximate nearest neighbors (RANN), found in src/mlpack/methods/rann/.

Addition of fast exact max-kernel search (FastMKS), found in src/mlpack/methods/fastmks/.

Fix for EM covariance estimation; this should improve GMM training time.

More parameters for GMM estimation.

Force GMM and GaussianDistribution covariance matrices to be positive definite, so that training converges much more often.

Add parameter for the tolerance of the Baum-Welch algorithm for HMM training.

Fix for compilation with clang compiler.

Fix for k-furthest-neighbor-search.

Force minimum Armadillo version to 2.4.2.

Better output of class types to streams; a class with a ToString() method implemented can be sent to a stream with operator<<. See #164.

Change return type of GMM::Estimate() to double (#266).

Style fixes for k-means and RADICAL.

Handle size_t support correctly with Armadillo 3.6.2 (#267).

Add locality-sensitive hashing (LSH), found in src/mlpack/methods/lsh/.

Better tests for SGD (stochastic gradient descent) and NCA (neighborhood components analysis).

Remove internal sparse matrix support because Armadillo 3.4.0 now includes it. When using Armadillo versions older than 3.4.0, sparse matrix support is not available.

NCA (neighborhood components analysis) now support an arbitrary optimizer (#254), including stochastic gradient descent (#258).

Added density estimation trees, found in src/mlpack/methods/det/.

Added non-negative matrix factorization, found in src/mlpack/methods/nmf/.

Added experimental cover tree implementation, found in src/mlpack/core/tree/cover_tree/ (#156).

Better reporting of boost::program_options errors (#231).

Fix for timers on Windows (#218, #217).

Fix for allknn and allkfn output (#210).

Sparse coding dictionary initialization is now a template parameter (#226).

Added kernel principal components analysis (kernel PCA), found in src/mlpack/methods/kernel_pca/ (#47).

Fix for Lovasz-Theta AugLagrangian tests (#188).

Fixes for allknn output (#191, #192).

Added range search executable (#198).

Adapted citations in documentation to BiBTeX; no citations in -h output (#201).

Stop use of 'const char*' and prefer 'std::string' (#183).

Support seeds for random numbers (#182).