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Train a PCA followed by a LDA

This algorithm is a **legacy** one. The API has changed since its implementation. New versions and forks will need to be updated.

Algorithms have at least one
**input** and one **output**. All
algorithm endpoints are organized in **groups**.
Groups are used by the platform to indicate which inputs and
outputs are synchronized together. The first group is
automatically synchronized with the channel defined by the
block in which the algorithm is deployed.

Endpoint Name | Data Format | Nature |
---|---|---|

image | system/array_2d_uint8/1 | Input |

client_id | system/uint64/1 | Input |

subspace_lda | tutorial/linear_machine/1 | Output |

subspace_pca | tutorial/linear_machine/1 | Output |

Parameters allow users to change the configuration of an algorithm when scheduling an experiment

Name | Description | Type | Default | Range/Choices |
---|---|---|---|---|

number-of-pca-components | uint32 | 5 | ||

number-of-lda-components | uint32 | 2 |

The code for this algorithm in Python

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This algorithm performs principal component analysis (PCA) [PCA] on a given dataset using the singular value decomposition (SVD) [SVD], followed by linear discriminant analysis (LDA) [LDA].

This implementation relies on the Bob library.

The inputs are:

- image: A training set of floating point vectors as a two-dimensional array of floats (64 bits), the number of rows corresponding to the number of training samples, and the number of columns to the dimensionality of the training samples.
- client_id: Client (class/subject) identifier as an unsigned 64 bits integer.

The outputs are subspace_pca and subspace_lda for the PCA and LDA transformation, respectively.

[SVD] | http://en.wikipedia.org/wiki/Singular_value_decomposition |

[PCA] | http://en.wikipedia.org/wiki/Principal_component_analysis |

[LDA] | http://en.wikipedia.org/wiki/Linear_discriminant_analysis |

No experiments are using this algorithm.

This algorithm was never executed.

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