Add references to the documentation.

docs/source/conf.py

@@ -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'

docs/source/library.rst

@@ -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:
+   :undoc-members:
+
+Bibliography
+========================================
+.. bibliography::

docs/source/refs.bib

@@ -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},
+}
+

prototorch/models/probabilistic.py

@@ -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)

setup.py

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