Add references to the documentation.

This commit is contained in:
Alexander Engelsberger 2021-05-26 21:20:17 +02:00
parent 0c1f7a4772
commit 66e3e51a52
5 changed files with 120 additions and 13 deletions

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@ -47,6 +47,7 @@ extensions = [
"sphinx.ext.viewcode",
"sphinx_rtd_theme",
"sphinxcontrib.katex",
"sphinxcontrib.bibtex",
]
# https://nbsphinx.readthedocs.io/en/0.8.5/custom-css.html#For-All-Pages
@ -202,3 +203,7 @@ intersphinx_mapping = {
epub_cover = ()
version = release
# -- Options for Bibliography -------------------------------------------
bibtex_bibfiles = ['refs.bib']
bibtex_reference_style = 'author_year'

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@ -14,7 +14,7 @@ Unsupervised Methods
Classical Learning Vector Quantization
-----------------------------------------
Original LVQ models by Kohonen.
Original LVQ models introduced by :cite:t:`kohonen1989`.
These heuristic algorithms do not use gradient descent.
.. autoclass:: prototorch.models.lvq.LVQ1
@ -22,7 +22,7 @@ These heuristic algorithms do not use gradient descent.
.. autoclass:: prototorch.models.lvq.LVQ21
:members:
It is also possible to use the GLVQ structure as shown in [Sato&Yamada].
It is also possible to use the GLVQ structure as shown by :cite:t:`sato1996` in chapter 4.
This allows the use of gradient descent methods.
.. autoclass:: prototorch.models.glvq.GLVQ1
@ -33,14 +33,15 @@ This allows the use of gradient descent methods.
Generalized Learning Vector Quantization
-----------------------------------------
:cite:t:`sato1996` presented a LVQ variant with a cost function called GLVQ.
This allows the use of gradient descent methods.
.. autoclass:: prototorch.models.glvq.GLVQ
:members:
.. autoclass:: prototorch.models.glvq.ImageGLVQ
:members:
.. autoclass:: prototorch.models.glvq.SiameseGLVQ
:members:
The cost function of GLVQ can be extended by a learnable dissimilarity.
These learnable dissimilarities assign relevances to each data dimension during the learning phase.
For example GRLVQ :cite:p:`hammer2002` and GMLVQ :cite:p:`schneider2009` .
.. autoclass:: prototorch.models.glvq.GRLVQ
:members:
@ -48,11 +49,44 @@ Generalized Learning Vector Quantization
.. autoclass:: prototorch.models.glvq.GMLVQ
:members:
The dissimilarity from GMLVQ can be interpreted as a projection into another dataspace.
Applying this projection only to the data results in LVQMLN
.. autoclass:: prototorch.models.glvq.LVQMLN
:members:
The projection idea from GMLVQ can be extended to an arbitrary transformation with learnable parameters.
.. autoclass:: prototorch.models.glvq.SiameseGLVQ
:members:
Probabilistic Models
--------------------------------------------
Probabilistic variants assume, that the prototypes generate a probability distribution over the classes.
For a test sample they return a distribution instead of a class assignment.
The following two algorihms were presented by :cite:t:`seo2003` .
Every prototypes is a center of a gaussian distribution of its class, generating a mixture model.
.. autoclass:: prototorch.models.probabilistic.LikelihoodRatioLVQ
:members:
.. autoclass:: prototorch.models.probabilistic.RSLVQ
:members:
Missing:
- PLVQ
Classification by Component
-----------------------------------------
--------------------------------------------
The Classification by Component (CBC) has been introduced by :cite:t:`saralajew2019` .
In a CBC architecture there is no class assigned to the prototypes.
Instead the dissimilarities are used in a reasoning process, that favours or rejects a class by a learnable degree.
The output of a CBC network is a probability distribution over all classes.
.. autoclass:: prototorch.models.cbc.CBC
:members:
@ -62,6 +96,15 @@ Classification by Component
Visualization
========================================
Visualization is very specific to its application.
PrototorchModels delivers visualization for two dimensional data and image data.
The visulizations can be shown in a seperate window and inside a tensorboard.
.. automodule:: prototorch.models.vis
:members:
:undoc-members:
Bibliography
========================================
.. bibliography::

62
docs/source/refs.bib Normal file
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@ -0,0 +1,62 @@
@article{sato1996,
title={Generalized learning vector quantization},
author={Sato, Atsushi and Yamada, Keiji},
journal={Advances in neural information processing systems},
pages={423--429},
year={1996},
publisher={MORGAN KAUFMANN PUBLISHERS},
url={http://papers.nips.cc/paper/1113-generalized-learning-vector-quantization.pdf},
}
@book{kohonen1989,
doi = {10.1007/978-3-642-88163-3},
year = {1989},
publisher = {Springer Berlin Heidelberg},
author = {Teuvo Kohonen},
title = {Self-Organization and Associative Memory}
}
@inproceedings{saralajew2019,
author = {Saralajew, Sascha and Holdijk, Lars and Rees, Maike and Asan, Ebubekir and Villmann, Thomas},
booktitle = {Advances in Neural Information Processing Systems},
title = {Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components},
url = {https://proceedings.neurips.cc/paper/2019/file/dca5672ff3444c7e997aa9a2c4eb2094-Paper.pdf},
volume = {32},
year = {2019}
}
@article{seo2003,
author = {Seo, Sambu and Obermayer, Klaus},
title = "{Soft Learning Vector Quantization}",
journal = {Neural Computation},
volume = {15},
number = {7},
pages = {1589-1604},
year = {2003},
month = {07},
doi = {10.1162/089976603321891819},
}
@article{hammer2002,
title = {Generalized relevance learning vector quantization},
journal = {Neural Networks},
volume = {15},
number = {8},
pages = {1059-1068},
year = {2002},
doi = {https://doi.org/10.1016/S0893-6080(02)00079-5},
author = {Barbara Hammer and Thomas Villmann},
}
@article{schneider2009,
author = {Schneider, Petra and Biehl, Michael and Hammer, Barbara},
title = "{Adaptive Relevance Matrices in Learning Vector Quantization}",
journal = {Neural Computation},
volume = {21},
number = {12},
pages = {3532-3561},
year = {2009},
month = {12},
doi = {10.1162/neco.2009.11-08-908},
}

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@ -90,8 +90,6 @@ def robust_soft_loss(probabilities, target, prototype_labels):
class LikelihoodRatioLVQ(GLVQ):
"""Learning Vector Quantization based on Likelihood Ratios
Based on "Soft Learning Vector Quantization" from Sambu Seo and Klaus Obermayer (2003).
"""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
@ -128,8 +126,6 @@ class LikelihoodRatioLVQ(GLVQ):
class RSLVQ(GLVQ):
"""Learning Vector Quantization based on Likelihood Ratios
Based on "Soft Learning Vector Quantization" from Sambu Seo and Klaus Obermayer (2003).
"""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)

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@ -33,6 +33,7 @@ DOCS = [
"nbsphinx",
"sphinx_rtd_theme",
"sphinxcontrib-katex",
"sphinxcontrib-bibtex",
]
EXAMPLES = [
"matplotlib",