Principal Component Analysis (PCA)
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 |
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.