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Forked from tutorial/pca/2

This algorithm is a **sequential** one. The platform will call its *process()* method once per data incoming on its inputs.

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 |

subspace | 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-components | uint32 | 5 |

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].

This implementation relies on the Bob library.

The input image is 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.

The output subspace is a linear transformation as a collection of weights, biases, input subtraction and input division factors.

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

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

Updated | Name | Databases/Protocols | Analyzers | |||
---|---|---|---|---|---|---|

amohammadi/amohammadi/atnt_eigenfaces/1/atnt1 | atnt/6@idiap | amohammadi/eer_analyzer/1 | ||||

amohammadi/tutorial/eigenface/1/atnt-eigenfaces-66-comp | atnt/5@idiap | tutorial/postperf_iso/1 | ||||

amohammadi/tutorial/eigenface/1/atnt-eigenfaces-67-comp | atnt/5@idiap | tutorial/postperf_iso/1 |

This table shows the number of times this algorithm
has been **successfully** run using the given environment. Note
this does not provide sufficient information to evaluate if the
algorithm will run when submitted to different conditions.

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