NMF (version 0.2.2)

NMF-class: Interface class for Nonnegative Matrix Factorisation models

Description

This is a virtual class that defines a common interface to handle Nonnegative Matrix Factorisation models (NMF models) in a generic way. It provides the definition for a minimum set of generic methods that are used in common computations and tasks in the context of Nonnegative Matrix Factorisations. Class NMF makes it easy to develop new models that integrates well into the general framework implemented by the NMF package. Following a few simple guidelines, new models benefit from all the functionalities available to built-in NMF models -- that derive themselves from class NMF. See section Defining new NMF models below. See section NMFstd, references and links therein for details on the standard NMF model and its -- built-in -- extensions.

Slots

This class doesn't contain any slot. Its purpose is to define a common interface for NMF models as a collection of generic methods. Classes that inherits from class NMF are responsible for the management of data storage and the implementation of the interface's pure virtual methods.

Objects from the Class

Strictly speaking, because class NMF is virtual, no object of class NMF can be instantiated, only objects from its sub-classes. However, those objects are sometimes shortly referred in the documentation as "NMF objects" instead of "objects that inherits from class NMF". For built-in models or for models that inherit from the standard model class NMFstd, the factory method newNMF enables to easily create valid NMF objects in a variety of common situations. See newNMF for more details.

References

Definition of Nonnegative Matrix Factorization in its modern formulation: Lee D.D. and Seung H.S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401, 788--791. Historical first definition and algorithms: Paatero, P., Tapper, U. (1994). Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics, 2, 111--126 , doi:10.1002/env.3170050203. Reference for some utility functions: Kim, H. and Park, H. (2007). Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis. Bioinformatics. Hoyer (2004). Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research, 5, 1457-1469.

See Also

Main interface to perform NMF in nmf-methods. Built-in NMF models and factory method in newNMF. Method seed to set NMF objects with values suitable to start algorithms with.

Examples

# show all the NMF models available (i.e. the classes that inherit from class NMF)
nmf.models()
# show all the built-in NMF models available
nmf.models(builtin.only=TRUE)

# class NMF is a virtual class so cannot be instantiated: 
# the following generates an error
new('NMF')

# To instantiate a NMF model, use factory method newNMF (see ?newNMF)
newNMF(3)
newNMF(3, model='NMFns')