Projects supporting the matlab data format.
Showing Items 21-40 of 67 on page 2 of 4: Previous 1 2 3 4 Next

Logo Multilinear Principal Component Analysis 1.3

by hplu - September 8, 2013, 13:04:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4336 views, 814 downloads, 1 subscription

About: A Matlab implementation of Multilinear PCA (MPCA) and MPCA+LDA for dimensionality reduction of tensor data with sample code on gait recognition

  1. The MPCA paper is updated with a typo (the MAD measure in Table II) corrected.

  2. Tensor toolbox version 2.1 is included for convenience.

  3. Full code on gait recognition is included for verification and comparison.

About: Stochastic neighbor embedding originally aims at the reconstruction of given distance relations in a low-dimensional Euclidean space. This can be regarded as general approach to multi-dimensional scaling, but the reconstruction is based on the definition of input (and output) neighborhood probability alone. The present implementation also allows for handling dissimilarity or score-induced neighborhood topologies and makes use of quasi 2nd order gradient-based (l-)BFGS optimization.

  • gradient in xsne_fun.m fixed! (constant factor m was missing)

  • symmetry option re-introduced allowing for enabling symmetric and asymmetric versions of SNE and t-SNE

Logo cbMDS Correlation Based Multi Dimensional Scaling 1.2

by emstrick - July 27, 2013, 14:35:36 CET [ BibTeX BibTeX for corresponding Paper Download ] 3663 views, 916 downloads, 1 subscription

About: The aim is to embed a given data relationship matrix into a low-dimensional Euclidean space such that the point distances / distance ranks correlate best with the original input relationships. Input relationships may be given as (sparse) (asymmetric) distance, dissimilarity, or (negative!) score matrices. Input-output relations are modeled as low-conditioned. (Weighted) Pearson and soft Spearman rank correlation, and unweighted soft Kendall correlation are supported correlation measures for input/output object neighborhood relationships.

  • Initial release (Ver 1.0): Weighted Pearson and correlation and soft Spearman rank correlation, Tue Dec 4 16:14:51 CET 2012

  • Ver 1.1 Added soft Kendall correlation, Fri Mar 8 08:41:09 CET 2013

  • Ver 1.2 Added reconstruction of sparse relationship matrices, Fri Jul 26 16:58:37 CEST 2013

Logo Thalasso v0.2

by rherault - July 22, 2013, 15:33:59 CET [ Project Homepage BibTeX Download ] 1065 views, 304 downloads, 1 subscription

About: Regularization paTH for LASSO problem (thalasso) thalasso solves problems of the following form: minimize 1/2||X*beta-y||^2 + lambda*sum|beta_i|, where X and y are problem data and beta and lambda are variables.


Initial Announcement on

About: This toolbox implements a novel visualization technique called Sectors on Sectors (SonS), and a extended version called Multidimensional Sectors on Sectors (MDSonS), for improving the interpretation of several data mining algorithms. The MDSonS method makes use of Multidimensional Scaling (MDS) to solve the main drawback of the previous method, namely, the lack of representing distances between pairs of clusters. These methods have been applied for visualizing the results of hierarchical clustering, Growing Hierarchical Self-Organizing Maps (GHSOM), classification trees and several manifolds. These methods make possible to extract all the existing relationships among centroids’ attributes at any hierarchy level.


Initial Announcement on

Logo Cognitive Foundry 3.3.3

by Baz - May 21, 2013, 05:59:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 17500 views, 2799 downloads, 2 subscriptions

About: The Cognitive Foundry is a modular Java software library of machine learning components and algorithms designed for research and applications.

  • General:
    • Made code able to compile under both Java 1.6 and 1.7. This required removing some potentially unsafe methods that used varargs with generics.
    • Upgraded XStream dependency to 1.4.4.
    • Improved support for regression algorithms in learning.
    • Added general-purpose adapters to make it easier to compose learning algorithms and adapt their input or output.
  • Common Core:
    • Added isSparse, toArray, dotDivide, and dotDivideEquals methods for Vector and Matrix.
    • Added scaledPlus, scaledPlusEquals, scaledMinus, and scaledMinusEquals to Ring (and thus Vector and Matrix) for potentially faster such operations.
    • Fixed issue where matrix and dense vector equals was not checking for equal dimensionality.
    • Added transform, transformEquals, tranformNonZeros, and transformNonZerosEquals to Vector.
    • Made LogNumber into a signed version of a log number and moved the prior unsigned implementation into UnsignedLogNumber.
    • Added EuclideanRing interface that provides methods for times, timesEquals, divide, and divideEquals. Also added Field interface that provides methods for inverse and inverseEquals. These interfaces are now implemented by the appropriate number classes such as ComplexNumber, MutableInteger, MutableLong, MutableDouble, LogNumber, and UnsignedLogNumber.
    • Added interface for Indexer and DefaultIndexer implementation for creating a zero-based indexing of values.
    • Added interfaces for MatrixFactoryContainer and DivergenceFunctionContainer.
    • Added ReversibleEvaluator, which various identity functions implement as well as a new utility class ForwardReverseEvaluatorPair to create a reversible evaluator from a pair of other evaluators.
    • Added method to create an ArrayList from a pair of values in CollectionUtil.
    • ArgumentChecker now properly throws assertion errors for NaN values. Also added checks for long types.
    • Fixed handling of Infinity in subtraction for LogMath.
    • Fixed issue with angle method that would cause a NaN if cosine had a rounding error.
    • Added new createMatrix methods to MatrixFactory that initializes the Matrix with the given value.
    • Added copy, reverse, and isEmpty methods for several array types to ArrayUtil.
    • Added utility methods for creating a HashMap, LinkedHashMap, HashSet, or LinkedHashSet with an expected size to CollectionUtil.
    • Added getFirst and getLast methods for List types to CollectionUtil.
    • Removed some calls to System.out and Exception.printStackTrace.
  • Common Data:
    • Added create method for IdentityDataConverter.
    • ReversibleDataConverter now is an extension of ReversibleEvaluator.
  • Learning Core:
    • Added general learner transformation capability to make it easier to adapt and compose algorithms. InputOutputTransformedBatchLearner provides this capability for supervised learning algorithms by composing together a triplet. CompositeBatchLearnerPair does it for a pair of algorithms.
    • Added a constant and identity learners.
    • Added Chebyshev, Identity, and Minkowski distance metrics.
    • Added methods to DatasetUtil to get the output values for a dataset and to compute the sum of weights.
    • Made generics more permissive for supervised cost functions.
    • Added ClusterDistanceEvaluator for taking a clustering that encodes the distance from an input value to all clusters and returns the result as a vector.
    • Fixed potential round-off issue in decision tree splitter.
    • Added random subspace technique, implemented in RandomSubspace.
    • Separated functionality from LinearFunction into IdentityScalarFunction. LinearFunction by default is the same, but has parameters that can change the slope and offset of the function.
    • Default squashing function for GeneralizedLinearModel and DifferentiableGeneralizedLinearModel is now a linear function instead of an atan function.
    • Added a weighted estimator for the Poisson distribution.
    • Added Regressor interface for evaluators that are the output of (single-output) regression learning algorithms. Existing such evaluators have been updated to implement this interface.
    • Added support for regression ensembles including additive and averaging ensembles with and without weights. Added a learner for regression bagging in BaggingRegressionLearner.
    • Added a simple univariate regression class in UnivariateLinearRegression.
    • MultivariateDecorrelator now is a VectorInputEvaluator and VectorOutputEvaluator.
    • Added bias term to PrimalEstimatedSubGradient.
  • Text Core:
    • Fixed issue with the start position for tokens from LetterNumberTokenizer being off by one except for the first one.

Logo OptWok 0.3.1

by ong - May 2, 2013, 10:46:11 CET [ Project Homepage BibTeX Download ] 7597 views, 1458 downloads, 1 subscription

About: A collection of python code to perform research in optimization. The aim is to provide reusable components that can be quickly applied to machine learning problems. Used in: - Ellipsoidal multiple instance learning - difference of convex functions algorithms for sparse classfication - Contextual bandits upper confidence bound algorithm (using GP) - learning output kernels, that is kernels between the labels of a classifier.

  • minor bugfix

Logo ChaLearn Gesture Challenge Turtle Tamers 1.0

by konkey - March 17, 2013, 18:39:22 CET [ BibTeX Download ] 1154 views, 463 downloads, 1 subscription

About: Soltion developed by team Turtle Tamers in the ChaLearn Gesture Challenge (


Initial Announcement on

Logo VLFeat 0.9.16

by andreavedaldi - October 5, 2012, 18:44:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6938 views, 1280 downloads, 1 subscription

About: The VLFeat open source library implements popular computer vision algorithms including affine covariant feature detectors, HOG, SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixels, and quick shift. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux. The latest version of VLFeat is 0.9.16.


VLFeat 0.9.16: Added VL_COVDET() (covariant feature detectors). This function implements the following detectors: DoG, Hessian, Harris Laplace, Hessian Laplace, Multiscale Hessian, Multiscale Harris. It also implements affine adaptation, estiamtion of feature orientation, computation of descriptors on the affine patches (including raw patches), and sourcing of custom feature frame. Addet the auxiliary function VL_PLOTSS(). This is the second point update supported by the PASCAL Harvest programme.

VLFeat 0.9.15: Added VL_HOG() (HOG features). Added VL_SVMPEGASOS() and a vastly improved SVM implementation. Added IHASHSUM (hashed counting). Improved INTHIST (integral histogram). Added VL_CUMMAX(). Improved the implementation of VL_ROC() and VL_PR(). Added VL_DET() (Detection Error Trade-off (DET) curves). Improved the verbosity control to AIB. Added support for Xcode 4.3, improved support for past and future Xcode versions. Completed the migration of the old test code in toolbox/test, moving the functionality to the new unit tests toolbox/xtest. Improved credits. This is the first point update supported by the PASCAL Harvest (several more to come shortly).

Logo SVMStructMATLAB 1.2

by andreavedaldi - September 12, 2012, 00:25:57 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8317 views, 1596 downloads, 1 subscription

About: svm-struct-matlab is a MATLAB wrapper of T. Joachims' SVM^struct solver for structured output support vector machines.


Adds support for Xcode 4.0 and Mac OS X 10.7 and greater.

Logo SVM with uncertain labels 0.2

by rflamary - July 17, 2012, 11:06:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5206 views, 1063 downloads, 2 subscriptions

About: Matlab code for learning probabilistic SVM in the presence of uncertain labels.


Added missing dataset function (thanks to Hao Wu)

Logo Uncorrelated Multilinear Discriminant Analysis 1.0

by hplu - July 7, 2012, 06:27:56 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3041 views, 559 downloads, 1 subscription

About: A Matlab implementation of Uncorrelated Multilinear Discriminant Analysis (UMLDA) for dimensionality reduction of tensor data via tensor-to-vector projection


Initial Announcement on

Logo Uncorrelated Multilinear Principal Component Analysis 1.0

by hplu - June 18, 2012, 17:23:52 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2510 views, 497 downloads, 1 subscription

About: A Matlab implementation of Uncorrelated Multilinear PCA (UMPCA) for dimensionality reduction of tensor data via tensor-to-vector projection


Initial Announcement on

About: The package provides a Lagrangian approach to the posterior regularization of given linear mappings. This is important in two cases, (a) when systems are under-determined and (b) when the external model for calculating the mapping is invariant to properties such as scaling. The software may be applied in cases when the external model does not provide its own regularization strategy. In addition, the package allows to rank attributes according to their distortion potential to a given linear mapping.


Version 1.1 (May 23, 2012) memory and time optimizations distderivrel.m now supports assessing the relevance of attribute pairs

Version 1.0 (Nov 9, 2011) * Initial Announcement on

Logo Output Kernel Learning 2.0

by posaune - March 22, 2012, 11:34:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5011 views, 799 downloads, 1 subscription

About: A Matlab script for learning vector-valued functions and kernels on the output space.


Added code for learning low-rank output kernels.

Logo GP RTSS 1.0

by marc - March 21, 2012, 08:43:52 CET [ BibTeX BibTeX for corresponding Paper Download ] 2069 views, 638 downloads, 1 subscription

About: Gaussian process RTS smoothing (forward-backward smoothing) based on moment matching.


Initial Announcement on

About: This local and parallel computation toolbox is the Octave and Matlab implementation of several localized Gaussian process regression methods: the domain decomposition method (Park et al., 2011, DDM), partial independent conditional (Snelson and Ghahramani, 2007, PIC), localized probabilistic regression (Urtasun and Darrell, 2008, LPR), and bagging for Gaussian process regression (Chen and Ren, 2009, BGP). Most of the localized regression methods can be applied for general machine learning problems although DDM is only applicable for spatial datasets. In addition, the GPLP provides two parallel computation versions of the domain decomposition method. The easiness of being parallelized is one of the advantages of the localized regression, and the two parallel implementations will provide a good guidance about how to materialize this advantage as software.


Initial Announcement on

Logo Sparse MultiTask Learning Toolbox 1.2

by rflamary - March 18, 2012, 11:31:00 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3270 views, 784 downloads, 1 subscription

About: This package is a set of Matlab scripts that implements the algorithms described in the submitted paper: "Lp-Lq Sparse Linear and Sparse Multiple Kernel MultiTask Learning".


Initial Announcement on

Logo multi assignment clustering of Boolean data 2.001

by mafrank - March 3, 2012, 09:04:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6232 views, 837 downloads, 1 subscription

About: Implementation of the multi-assignment clustering method for Boolean vectors.


new bib added

Logo Large margin filtering 0.9

by rflamary - February 18, 2012, 15:50:43 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2797 views, 612 downloads, 1 subscription

About: Matlab SVM toolbox for learning large margin filters in signal or images.


Initial Announcement on

Showing Items 21-40 of 67 on page 2 of 4: Previous 1 2 3 4 Next