shark::NormalizeComponentsZCA Class Reference

Train a linear model to whiten the data. More...

#include <shark/Algorithms/Trainers/NormalizeComponentsZCA.h>

+ Inheritance diagram for shark::NormalizeComponentsZCA:

Public Member Functions

 NormalizeComponentsZCA (double targetVariance=1.0)
 
std::string name () const
 From INameable: return the class name. More...
 
void train (ModelType &model, UnlabeledData< RealVector > const &input)
 
- Public Member Functions inherited from shark::AbstractUnsupervisedTrainer< LinearModel< RealVector > >
virtual void train (ModelType &model, const UnlabeledData< InputType > &inputset)=0
 Core of the Trainer interface. More...
 
- Public Member Functions inherited from shark::INameable
virtual ~INameable ()
 
- Public Member Functions inherited from shark::ISerializable
virtual ~ISerializable ()
 Virtual d'tor. More...
 
virtual void read (InArchive &archive)
 Read the component from the supplied archive. More...
 
virtual void write (OutArchive &archive) const
 Write the component to the supplied archive. More...
 
void load (InArchive &archive, unsigned int version)
 Versioned loading of components, calls read(...). More...
 
void save (OutArchive &archive, unsigned int version) const
 Versioned storing of components, calls write(...). More...
 
 BOOST_SERIALIZATION_SPLIT_MEMBER ()
 

Public Attributes

double m_targetVariance
 

Additional Inherited Members

- Public Types inherited from shark::AbstractUnsupervisedTrainer< LinearModel< RealVector > >
typedef LinearModel< RealVector > ModelType
 
typedef LinearModel< RealVector > ::InputType InputType
 

Detailed Description

Train a linear model to whiten the data.

ZCA does whitening in the sense that it sets the mean to zero and the covariance to the Identity. However in contrast to NormalizeComponentsWhitening it makes sure that the initial and end coordinate system are the same and just rescales the data. The effect is, that image data still resembles images after applying ZCA in contrast to other methods which rotate the data randomly.

Definition at line 54 of file NormalizeComponentsZCA.h.

Constructor & Destructor Documentation

◆ NormalizeComponentsZCA()

shark::NormalizeComponentsZCA::NormalizeComponentsZCA ( double  targetVariance = 1.0)
inline

Definition at line 60 of file NormalizeComponentsZCA.h.

References SHARK_RUNTIME_CHECK.

Member Function Documentation

◆ name()

std::string shark::NormalizeComponentsZCA::name ( ) const
inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 66 of file NormalizeComponentsZCA.h.

◆ train()

Member Data Documentation

◆ m_targetVariance

double shark::NormalizeComponentsZCA::m_targetVariance

Definition at line 59 of file NormalizeComponentsZCA.h.


The documentation for this class was generated from the following file: