118 lines
3.6 KiB
ReStructuredText
118 lines
3.6 KiB
ReStructuredText
.. Available Models
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Models
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========================================
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.. image:: _static/img/model_tree.png
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:width: 600
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Unsupervised Methods
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-----------------------------------------
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.. autoclass:: prototorch.models.knn.KNN
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:members:
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.. autoclass:: prototorch.models.unsupervised.NeuralGas
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:members:
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.. autoclass:: prototorch.models.unsupervised.GrowingNeuralGas
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:members:
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Classical Learning Vector Quantization
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-----------------------------------------
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Original LVQ models introduced by :cite:t:`kohonen1989`.
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These heuristic algorithms do not use gradient descent.
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.. autoclass:: prototorch.models.lvq.LVQ1
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:members:
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.. autoclass:: prototorch.models.lvq.LVQ21
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:members:
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It is also possible to use the GLVQ structure as shown by :cite:t:`sato1996` in chapter 4.
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This allows the use of gradient descent methods.
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.. autoclass:: prototorch.models.glvq.GLVQ1
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:members:
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.. autoclass:: prototorch.models.glvq.GLVQ21
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:members:
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Generalized Learning Vector Quantization
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-----------------------------------------
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:cite:t:`sato1996` presented a LVQ variant with a cost function called GLVQ.
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This allows the use of gradient descent methods.
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.. autoclass:: prototorch.models.glvq.GLVQ
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:members:
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The cost function of GLVQ can be extended by a learnable dissimilarity.
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These learnable dissimilarities assign relevances to each data dimension during the learning phase.
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For example GRLVQ :cite:p:`hammer2002` and GMLVQ :cite:p:`schneider2009` .
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.. autoclass:: prototorch.models.glvq.GRLVQ
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:members:
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.. autoclass:: prototorch.models.glvq.GMLVQ
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:members:
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The dissimilarity from GMLVQ can be interpreted as a projection into another dataspace.
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Applying this projection only to the data results in LVQMLN
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.. autoclass:: prototorch.models.glvq.LVQMLN
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:members:
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The projection idea from GMLVQ can be extended to an arbitrary transformation with learnable parameters.
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.. autoclass:: prototorch.models.glvq.SiameseGLVQ
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:members:
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Probabilistic Models
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--------------------------------------------
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Probabilistic variants assume, that the prototypes generate a probability distribution over the classes.
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For a test sample they return a distribution instead of a class assignment.
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The following two algorithms were presented by :cite:t:`seo2003` .
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Every prototypes is a center of a gaussian distribution of its class, generating a mixture model.
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.. autoclass:: prototorch.models.probabilistic.SLVQ
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:members:
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.. autoclass:: prototorch.models.probabilistic.RSLVQ
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:members:
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:cite:t:`villmann2018` proposed two changes to RSLVQ: First incorporate the winning rank into the prior probability calculation.
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And second use divergence as loss function.
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.. autoclass:: prototorch.models.probabilistic.PLVQ
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:members:
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Classification by Component
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--------------------------------------------
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The Classification by Component (CBC) has been introduced by :cite:t:`saralajew2019` .
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In a CBC architecture there is no class assigned to the prototypes.
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Instead the dissimilarities are used in a reasoning process, that favours or rejects a class by a learnable degree.
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The output of a CBC network is a probability distribution over all classes.
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.. autoclass:: prototorch.models.cbc.CBC
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:members:
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.. autoclass:: prototorch.models.cbc.ImageCBC
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:members:
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Visualization
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========================================
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Visualization is very specific to its application.
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PrototorchModels delivers visualization for two dimensional data and image data.
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The visualizations can be shown in a separate window and inside a tensorboard.
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.. automodule:: prototorch.models.vis
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:members:
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:undoc-members:
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Bibliography
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========================================
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.. bibliography::
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