- General
- Developers
- Mahout-Samsara
- Algorithms
- List of algorithms
- Distributed Matrix Decomposition
- Cholesky QR
- SSVD
- Distributed ALS
- SPCA
- Recommendations
- Recommender Overview
- Intro to cooccurrence-based
recommendations with Spark - Classification
- Spark Naive Bayes
- MapReduce Basics
- List of algorithms
- Overview
- Working with text
- Creating vectors from text
- Collocations
- Dimensionality reduction
- Singular Value Decomposition
- Stochastic SVD
- Topic Models
- Latent Dirichlet Allocation
- Mahout MapReduce
- Classification
- Naive Bayes
- Hidden Markov Models
- Logistic Regression (Single Machine)
- Random Forest
- Classification Examples
- Breiman example
- 20 newsgroups example
- SGD classifier bank marketing
- Wikipedia XML parser and classifier
- Clustering
- k-Means
- Canopy
- Fuzzy k-Means
- Streaming KMeans
- Spectral Clustering
- Clustering Commandline usage
- Options for k-Means
- Options for Canopy
- Options for Fuzzy k-Means
- Clustering Examples
- Synthetic data
- Cluster Post processing
- Cluster Dumper tool
- Cluster visualisation
- Recommendations
- First Timer FAQ
- A user-based recommender
in 5 minutes - Matrix factorization-based
recommenders - Overview
- Intro to item-based recommendations
with Hadoop - Intro to ALS recommendations
with Hadoop
NOTE: This implementation is a Work-In-Progress, at least till September,
The JIRA issue is here .
Boltzmann Machines
Boltzmann Machines are a type of stochastic neural networks that closely resemble physical processes. They define a network of units with an overall energy that is evolved over a period of time, until it reaches thermal equilibrium.
However, the convergence speed of Boltzmann machines that have unconstrained connectivity is low.
Restricted Boltzmann Machines
Restricted Boltzmann Machines are a variant, that are ‘restricted’ in the sense that connections between hidden units of a single layer are not allowed. In addition, stacking multiple RBM’s is also feasible, with the activities of the hidden units forming the base for a higher-level RBM. The combination of these two features renders RBM’s highly usable for parallelization.
In the Netflix Prize, RBM’s offered distinctly orthogonal predictions to SVD and k-NN approaches, and contributed immensely to the final solution.
RBM’s in Apache Mahout
An implementation of Restricted Boltzmann Machines is being developed for Apache Mahout as a Google Summer of Code 2010 project. A recommender interface will also be provided. The key aims of the implementation are:
- Accurate - should replicate known results, including those of the Netflix Prize
- Fast - The implementation uses Map-Reduce, hence, it should be fast
- Scale - Should scale to large datasets, with a design whose critical parts don’t need a dependency between the amount of memory on your cluster systems and the size of your dataset
You can view the patch as it develops here .