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# [MRG+2] Neighborhood Components Analysis #10058

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merged 89 commits into from Feb 28, 2019
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# [MRG+2] Neighborhood Components Analysis#10058

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#### Just for now

correct objective function doc

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wdevazelhes committed Jan 15, 2018
commit 8b5646c0552f74a492c4070aec91b0354f8a39f4
@@ -653,13 +653,13 @@ each method. Each data sample belongs to one of 10 classes.
Mathematical formulation
------------------------

The goal of NCA is to learn an optimal linear transformation matrix :math:L^*
of size (n_features_out, n_features), which maximises in average the
probability :math:p_i of sample :math:i being correctly classified, i.e.:
The goal of NCA is to learn an optimal linear transformation matrix of size
(n_features_out, n_features), which maximises in average the probability
:math:p_i of sample :math:i being correctly classified, i.e.:

.. math::
L^*= \max\limits_{L} \sum\limits_{i=0}^{N - 1} p_{i}
\underset{L}{\arg\max} \sum\limits_{i=0}^{N - 1} p_{i}
with :math:N = n_samples and :math:p_i the probability of sample
:math:i being correctly classified according to a stochastic nearest
@@ -700,7 +700,7 @@ optimisation method, it currently uses scipy's l-bfgs-b with a full gradient
computation at each iteration, to avoid to tune the learning rate and provide
stable learning.

See the examples below and the docstring of
See the examples below and the doc string of

Member

docstring

#### wdevazelhes Feb 27, 2019

Author Contributor

Thanks, done

:meth:NeighborhoodComponentsAnalysis.fit for further information.

Complexity
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