[CODE] Dahua Lin贡献的两个开源软件

Dahua Lin大家应该比较清楚，CV界的牛人，MIT的博士，在港中文读硕士的时候就发了11篇一作的文章，包括PAMI和CVPR，博士期间更是牛，2010年NIPS best student paper。他的主页上也贡献了两个开源的软件。

 

Basic Computation Supporting Library (BCSLib)

This is a C++ library to provide basic support to various computation tasks. The library comprises the following modules:

• Base module: basic algorithms, memory management support, and meta-programming support
• Array module: template classes to represent vectors, matrices, vector and matrix views using different indexing, as well as functions to manipulate vectors and matrices.
• Data structure module: template classes and functions that implement various data structures, such as heaps, disjoint sets, and different types of trees.
• Graph module: template classes to represent graphs, and generic graph algorithms including BFS, DFS, topological sorting, shortest path algorithms, and minimum spanning tree algorithms.

Code website: 

 

Statistical Modeling and Inference Toolbox

This is a MATLAB Toolbox to support statistical learning and inference. It comprises over 300 functions, ranging from classic algorithms such as Principle Component Analysis (PCA) and K-Means, to advanced probabilistic models such as Latent Dirichlet Allocation and nonparametric Bayesian. Specifically, the toolbox is composed of the following modules:

• Base module: basic but useful functions for data manipulation, pairwise metric computation, and efficient routines to deal with the calculation on large amount of small matrices.
• Optim module: functions for nonlinear optimization (e.g. Newton and BFGS), and wrappers for LP\QP solvers, as well as functions to solve optimization problems of specific forms
• Classic module: a collection of classic algorithms
  • KNN-classification, K-means and K-medoid clustering, ROC Analysis
  • Linear regression, Logistic regression, Robust regression, Generic regularized loss minimization
  • Subspace learning: Principal Component Analysis, Fisher Linear Discriminant Analysis, Independent Component Analysis
  • Support vector machines for classification, regression, and ranking.
• Graph module: a collection of functions to solve graph-related problems.
  • Functions to efficiently represent and manipulate graphs
  • Classic graph algorithms: BFS, DFS, Connect components, Shortest paths, Minimum spanning trees, etc.
  • Network flow algorithms: maximum flow minimum cut.
  • Algorihms based on spectral graph theory: Graph embedding and normalized cut.
• P-Model module: classes and functions for probabilistic modeling and inference.
  • Evaluation and sampling of well-known distributions: Gaussian distribution, Gamma distribution, Dirichlet distribution, Wishart distribution, and inverse Wishart distribution, etc. This toolbox provides more efficient implementation than MATLAB's statistics toolbox on many functions.
  • Markov models: Markov chains, Markov random fields and Hidden Markov Models
  • Finite mixture models and Dirichlet process Mixture models.
  • Topic models, such as Latent Dirichlet Allocation.

Code website: 

 

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