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# Toolkits Overview | ||
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## Collaborative Filtering | ||
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 | ||
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The collaborative filtering toolkit provides tools to identify patterns of user interests and make targeted recommendations. Learn more about collaborative filtering [here.](http://en.wikipedia.org/wiki/Collaborative_Filter) | ||
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 | ||
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Most of the algorithms take the rating matrix R, which is a sparse matrix holding the rating given by users to movies, and builds a linear model, finding two low dimensional matrices | ||
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U and V s.t. their product approximates R: R ~ UV. | ||
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We implement multiple collaborative filtering algorithms: ALS (Alternating least squares), SGD (Stochastic Gradient Descent) , Bias SGD, Weighted-ALS, Sparse-ALS, SVD++. | ||
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## Clustering | ||
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 | ||
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## KMeans++ | ||
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GraphLab PowerGraph has a fast scalable implementation of the [Kmeans++](http://en.wikipedia.org/wiki/K-means%2B%2B) algorithm: a robust method of grouping datapoints into clusters. | ||
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## Computer Vision | ||
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 | ||
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The Computer Vision Toolkit aims to provide fully distributed wrappers to algorithms in [OpenCV](http://opencv.org/ "OpenCV"), an open-source library aimed at real-time computer vision. Currently, the only implemented algorithm is Image-Stitching, where the goal is to create a composite panoramic image from a collection of images. Learn more about computer vision [here.](http://en.wikipedia.org/wiki/Computer_vision) | ||
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GraphLab PowerGraph Computer Vision Toolkit has become it’s own spin-off project called [CloudCV](http://cloudcv.org "CloudCV"), a comprehensive system that will aims to provide access to state-of-the-art computer vision algorithms on the cloud. | ||
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 | ||
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CloudCV: Large-Scale Parallel Computer Vision on the Cloud | ||
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## Graphical Models | ||
 | ||
 | ||
 | ||
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[Graphical models](http://en.wikipedia.org/wiki/Graphical_model) provide a compact interpretable representation of complex statistical phenomena by encoding random variables as vertices in a graph and relationships between those variables as edges. The Graphical Models toolkit provides a collection of methods to make predictions under uncertainty, and for reasoning about structured noisy data. | ||
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The main components of Graphical Models toolkit are: | ||
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1. Distributed Dual Decomposition: performs maximum _a posteriori_ (MAP) inference in general [Markov Random Fields](http://en.wikipedia.org/wiki/Markov_random_field) via the Dual Decomposition algorithm. The MRF is assumed to be provided in the standard [UAI file format](http://www.cs.huji.ac.il/project/PASCAL/fileFormat.php). Maintained by [Dhruv Batra](http://filebox.ece.vt.edu/~dbatra/). | ||
2. [Structured Prediction](http://docs.graphlab.org/graphical_models.html): that applies the [Loopy Belief propagation (LBP)](http://en.wikipedia.org/wiki/Belief_propagation) algorithm to a pair-wise [Markov Random Field](http://en.wikipedia.org/wiki/Markov_random_field) encoding the classic [Potts Model](http://en.wikipedia.org/wiki/Potts_model). | ||
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## Graph Analytics | ||
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 | ||
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The Graph Analytics Toolkit aims to provide high performance, distributed tools for graph mining, for use in community detection, social network discovery, etc. | ||
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The toolkit currently implements the following tools: | ||
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### Triangle Counting | ||
 | ||
 | ||
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Two triangle counting program: | ||
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1. **Undirected Triangle Counting**: counts the total number of triangles in a graph, or the the number of triangles each vertex is in | ||
2. **Directed Triangle Counting:** Counts the number of types of triangles each vertex is in | ||
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### PageRank | ||
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 | ||
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A classical graph algorithm which assigns each vertex a numerical importance value based on random walk properties. Learn more about page rank [here.](http://en.wikipedia.org/wiki/PageRank) | ||
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### KCore Decomposition | ||
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 | ||
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Identifies a hierarchical ordering of the vertices in the graph, allowing discovery of the central components of the network. | ||
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## Topic Modeling | ||
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 | ||
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## Latent Dirichlet Allocation | ||
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The topic modelling toolbox currently implemented the [Latent Dirichlet Allocation](http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation) algorithm for deriving semantic topic information from a corpus of plain text. | ||
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## Linear Solvers | ||
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GraphLab PowerGraph iterative solvers, for solving a linear system of the type Ax = b. | ||
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Currently [Jacobi method](http://en.wikipedia.org/wiki/Jacobi_method) is implemented. |
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/** | ||
/** | ||
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\page clustering Clustering | ||
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