[ VIGRA Homepage | Function Index | Class Index | Namespaces | File List | Main Page ]

| Unsupervised Decomposition |

## Classes

class | PLSAOptions
| Option object for the pLSA algorithm. More...

## Functions

template<... >
void | pLSA (...)
| Decompose a matrix according to the pLSA algorithm. More...
template<class T , class C1 , class C2 , class C3 >
void | principalComponents (MultiArrayView< 2, T, C1 > const &features, MultiArrayView< 2, T, C2 > fz, MultiArrayView< 2, T, C3 > zv)
| Decompose a matrix according to the PCA algorithm. More...

## Detailed Description

Unsupervised matrix decomposition methods.

## Function Documentation

## ◆ principalComponents()

void vigra::principalComponents | ( | MultiArrayView< 2, T, C1 > const & | features,
|  | MultiArrayView< 2, T, C2 > | fz,
|  | MultiArrayView< 2, T, C3 > | zv
| ) |  |

Decompose a matrix according to the PCA algorithm.

This function implements the PCA algorithm (principal component analysis).

- features must be a matrix with shape `(numFeatures * numSamples)`, which is decomposed into the matrices
- fz with shape `(numFeatures * numComponents)` and
- zv with shape `(numComponents * numSamples)`

such that

(this formula requires that the features have been centered around the mean by `linalg::prepareRows (features, features, ZeroMean)`).

The shape parameter `numComponents` determines the complexity of the decomposition model and therefore the approximation quality (if `numComponents == numFeatures`, the representation becomes exact). Intuitively, `fz` is a projection matrix from the reduced space into the original space, and `zv` is the reduced representation of the data, using just `numComponents` features.

Declaration:

#include <vigra/unsupervised_decomposition.hxx>

namespace vigra {
template <class U, class C1, class C2, class C3>
void
principalComponents(MultiArrayView<2, U, C1> const & features,
MultiArrayView<2, U, C2> fz,
MultiArrayView<2, U, C3> zv);
}

Usage:

Matrix<double> data(numFeatures, numSamples);
... // fill the input matrix
int numComponents = 3;
Matrix<double> fz(numFeatures, numComponents),
zv(numComponents, numSamples);
// center the data
prepareRows(data, data, ZeroMean);
// compute the reduced representation
principalComponents(data, fz, zv);
Matrix<double> model = fz*zv;
double meanSquaredError = squaredNorm(data - model) / numSamples;

## ◆ pLSA()

void vigra::pLSA | ( |  | ... | ) |

Decompose a matrix according to the pLSA algorithm.

This function implements the pLSA algorithm (probabilistic latent semantic analysis) proposed in

T. Hofmann: "Probabilistic Latent Semantic Analysis", in: UAI'99, Proc. 15th Conf. on Uncertainty in Artificial Intelligence, pp. 289-296, Morgan Kaufmann, 1999

- features must be a matrix with shape `(numFeatures * numSamples)` and non-negative entries, which is decomposed into the matrices
- fz with shape `(numFeatures * numComponents)` and
- zv with shape `(numComponents * numSamples)`

such that

(this formula applies when pLSA is called with `PLSAOptions.normalizedComponentWeights(false)`. Otherwise, you must normalize the features by calling `linalg::prepareColumns (features, features, UnitSum)` to make the formula hold).

The shape parameter `numComponents` determines the complexity of the decomposition model and therefore the approximation quality. Intuitively, features are a set of words, and the samples a set of documents. The entries of the `features` matrix denote the relative frequency of the words in each document. The components represents a (presumably small) set of topics. The matrix `fz` encodes the relative frequency of words in the different topics, and the matrix `zv` encodes to what extend each topic explains the content of each document.

The option object determines the iteration termination conditions and the output normalization. In addition, you may pass a random number generator to pLSA() which is used to create the initial solution.

Declarations:

#include <vigra/unsupervised_decomposition.hxx>

namespace vigra {
template <class U, class C1, class C2, class C3, class Random>
void
pLSA(MultiArrayView<2, U, C1> const & features,
MultiArrayView<2, U, C2> & fz,
MultiArrayView<2, U, C3> & zv,
Random const& random,
PLSAOptions const & options = PLSAOptions());
template <class U, class C1, class C2, class C3>
void
pLSA(MultiArrayView<2, U, C1> const & features,
MultiArrayView<2, U, C2> & fz,
MultiArrayView<2, U, C3> & zv,
PLSAOptions const & options = PLSAOptions());
}

Usage:

Matrix<double> words(numWords, numDocuments);
... // fill the input matrix
int numTopics = 3;
Matrix<double> fz(numWords, numTopics),
zv(numTopics, numDocuments);
pLSA(words, fz, zv, PLSAOptions().normalizedComponentWeights(false));
Matrix<double> model = fz*zv;
double meanSquaredError = (words - model).squaredNorm() / numDocuments;

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany | html generated using doxygen and Python vigra 1.11.0