### algorithms tutorial linear_machine_projection 3

Accumulate images and apply a linear transformation

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

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.

#### Group: main

Endpoint Name Data Format Nature
image system/array_2d_uint8/1 Input
id system/uint64/1 Input
projections system/array_2d_floats/1 Output

#### Unnamed group

Endpoint Name Data Format Nature
subspace tutorial/linear_machine/1 Input

The code for this algorithm in Python
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This algorithm linearizes and accumulates images into a buffer, before applying a linear transformation (e.g. using a projection matrix computed by principal component analysis). The linear transformation relies on the Bob library.

The inputs are:

• image: an image as a two-dimensional arrays of floats (64 bits)
• id: an identifier which is used as follows: all images with the
same identifier are accumulated into the same buffer
• subspace: a linear transformation as a collection of weights,
biases, input subtraction and input division factors.

The output projections is a two-dimensional array of floats (64 bits), the number of rows corresponding to the number of accumulated images (with the same identifier), and the number of columns to the output dimensionality after applying the linear transformation.

#### Experiments

Updated Name Databases/Protocols Analyzers
smarcel/tutorial/eigenface_with_preprocessing/1/eigenface-prepro-tutorial-pca15-ter atnt/1@idiap tutorial/postperf_iso/1
anjos/tutorial/eigenface_with_preprocessing/1/foorbar atnt/1@idiap tutorial/postperf_iso/1
pranayd/pranayd/pranay/1/pranay atnt/1@idiap tutorial/postperf_iso/1
pkorshunov/tutorial/eigenface/1/eigenface-with-8-components atnt/1@idiap tutorial/postperf_iso/1
pedro/tutorial/eigenface_with_preprocessing/1/eigenface-with-51-components atnt/1@idiap tutorial/postperf_iso/1
EderKapisch/EderKapisch/eigenface_preproc/1/eigenface-with-10-components atnt/1@idiap tutorial/postperf_iso/1
tutorial/tutorial/eigenface_with_preprocessing/1/eigenface-with-10-components-preproc atnt/1@idiap tutorial/postperf_iso/1
AntonioCandia/AntonioCandia/eigenface_preprop/1/eigenface-with-10-components atnt/1@idiap tutorial/postperf/1
jastuchi/tutorial/eigenface/1/eigenface-with-11-components atnt/1@idiap tutorial/postperf_iso/1
marcus/tutorial/eigenface/1/eigenface-with-23-components atnt/1@idiap tutorial/postperf_iso/1
tutorial/tutorial/eigenface_with_preprocessing/1/eigenface-with-preproc-15 atnt/1@idiap tutorial/postperf_iso/1
murilovarges/tutorial/eigenface/1/eigenfaces_15comp_unesp atnt/1@idiap tutorial/postperf_iso/1
kgrm/tutorial/eigenface/1/eigenfaces_11comp atnt/1@idiap tutorial/postperf_iso/1
tutorial/tutorial/full_eigenface/1/mobioMale_eigenfaces_50comp mobio/1@male tutorial/eerhter_postperf_iso/1
anjos/tutorial/eigenface/1/demo42 atnt/1@idiap tutorial/postperf/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.