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QSNE

Fast nonlinear dimensionality reduction using a quadratic-convergence t-SNE algorithm.

Version 1.0
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Categories Multivariate Statistics
Authors Antti Hakkinen (antti.e.hakkinen@helsinki.fi)
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Requires liblapack3 (DEB) ; installer (bash)
Source files component.xml qsne.bash
Usage Example with default values

Inputs

Name Type Mandatory Description
in CSV Mandatory Input matrix. Rows represent samples and columns the input variables. All variables are used in the mapping.
init CSV Optional (Optional) initial guess. Rows represent samples and columns the projected variables. If a file is not provided, an initial guess is derived using a truncated SVD of the input matrix (PCA).

Outputs

Name Type Description
out CSV Output matrix. Rows represent samples and columns the projected variables.

Parameters

Name Type Default Description
compat boolean false t-SNE compatibility mode. Causes q-SNE to switch to gradient ascent and scales variables to those used in van der Maaten's t-SNE implementation. Note that in this mode, the convergence is linear.
cost_tol float 1.48e-9 Objective tolerance for detecting a stall in optimization. This allows q-SNE to stop early, when the objective no longer decreases.
dims int 2 Output dimension. Values of 2 and 3 are useful for visualization, but any number can be used.
max_iter int 100 Maximum number of iterations to perform. q-SNE requires roughly sqrt(n) iterations compared to a regular t-SNE implementation.
num_threads int -1 Number of threads executing in parallel. By default, a single thread for each core is used.
perplexity float 30 Perplexity. Controls roughly the number of neighbor samples affecting each sample.
perplexity_range float 0 Perplexity range. An optimal perplexity is automatically sought in the range [p-r/2,p+r/2] where p is the specified perplexity and r is the range.
rank int 10 Rank of the approximate Hessian. A value of 0 implies plain gradient ascent (the original t-SNE algorithm) and larger values are required for quadratic convergence. Should be roughly the local inherent dimension.

Test cases

Test case Parameters IN
in
IN
init
OUT
out
iris properties in (missing) out

dims=2,
perplexity=5


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