Self-organizing adaptive map: Autonomous learning of curves and
surfaces from point samples
Presenter : YAN-SHOU SIE
Authors : MARCO PIASTRA
2013. NN
Intelligent Database Systems Lab
Outlines
 Motivation
 Objectives
 Methodology
 Experiments
 Conclusions
 Comments
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Motivation
• In here we want from a point cloud image to
reconstruct it original structure, but preliminary
version SOAM algorithm is can not effective to
produced the expected topology.
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Objectives
• In here we present a improve version SOAM
algorithm, its has a much more predictability and
includes some new concepts.
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Methodology
• Topological and geometrical background Term
– homeomorphic
– manifold
– Voronoi cell
– Delaunay triangulation
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Methodology
– Restricted Delaunay complex ：
– Homeomorphism and ε –sample
– Witness complex
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Methodology
– Finite sets of witnesses and noise
• Growing self-organizing networks
– Positioning the units: ‘gas-like’ dynamics
• adaptation strategy of the first kind
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Methodology
• second kind of strategy
– Competitive Hebbian learning and dynamic units
– Growing networks, insertion threshold
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Methodology
• Self-Organizing Adaptive Map (SOAM)
– Stateful units
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Methodology
– Adaptive insertion thresholds
– The SOAM algorithm
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Methodology-distance measures
-document co-occurrence similarity
-window-based similarity
 Suppose that we have a document with four concepts: ‘Ad,’‘Bert,’ ‘Cees,’ and ‘Dirk.’
If the window size is 2, the following windows are created for this document:
{Ad}, {Ad, Bert}, {Bert, Cees},{Cees, Dirk}, and {Dirk}.
ex : ‘System’ appears in documents {1,3,6,8} and windows {1,5,10,14,18,20,28};
‘Process’ appears in documents {1,3,6,12} and windows {1,5,12,14,18,25,30}.
document similarity :
window similarity :
 the similarities are converted to distances:
Avg = 0.15
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Experiments
• Experimental setup
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Experiments
– Algorithm behavior
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Experiments
– Performances
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Experiments
– Undersampling and noise: when things go wrong
– Boundaries and non-manifold units
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Conclusions
• The SOAM algorithm represents an interesting alternative
to deformable models in that it can effectively deal with
changes in topology and execution speedup.
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Comments
• Advantages
-SOAM can be dynamically self-growth, and the results
will be generated close to the result we want, for the
field of 3D technology has considerable value..
• Applications
- medical imaging , 3D sample, etc.
Intelligent Database Systems Lab