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NMFGdClsUpdate Class

Class NMFGdClsUpdate encapsulates the Gradient Descent - Constrained Least Squares (GDCLS) algorithm for Nonnegative Matrix Facotorization (NMF).
Inheritance Hierarchy
SystemObject
  CenterSpace.NMath.CoreNMFGdClsUpdate

Namespace: CenterSpace.NMath.Core
Assembly: NMath (in NMath.dll) Version: 7.4
Syntax
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[SerializableAttribute]
public class NMFGdClsUpdate : INMFUpdateAlgorithm, 
	ICloneable

The NMFGdClsUpdate type exposes the following members.

Constructors
 NameDescription
Public methodNMFGdClsUpdate Creates the update with default lambda of zero.
Public methodNMFGdClsUpdate(Double) Creates the update with specified lambda value.
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Properties
 NameDescription
Public propertyLambda Returns the lambda value.
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Methods
 NameDescription
Public methodClone Creates a deep copy of this NMFGdClsUpdate.
Public methodCost Returns the value of the cost function for the non-negative factorization of V = WH. The cost function is minimized by the factorization.
Public methodInitialFactors Provides the initial factors for the iteration process that computes W and H in the non-negative matrix factorization V = WH.
Public methodIterationStep Updates the factors W and H in the non-negative matrix factorization V = WH.
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Fields
 NameDescription
Protected fieldStatic memberDEFAULT_LAMBDA Default lambda.
Protected fieldlambda_ Lambda.
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Remarks
In some cases it may be desirable to enforce a statistical sparsity constraint on the H matrix. As the sparsity of H increases, the basis vectors become more localized, i.e, the parts-based representation of the data in W becomes more and more enhanced. The Gradient Descent - Constrained Least Squares (GDCLS) algorithm enforces sparsity in H using a scheme that penalizes the number of non-zero entries in H. It is a hybrid algorithm that uses the multiplicative update rule for updating W while H is calculated using a constrained least squares model as the metric.
See Also