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SOM – self organizing maps
About
- a subtype of artificial
neural networks
- unsupervised learning
- vector quantization
- first described by the
Finnish professor Teuvo Kohonen Illustration
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SOM for Cell Broadband Engine Petr Hafner (hafnep1@fel.cvut.cz) SOM - - PowerPoint PPT Presentation
SOM for Cell Broadband Engine Petr Hafner (hafnep1@fel.cvut.cz) SOM - - PowerPoint PPT Presentation
IBM - CVUT Student Research Projects SOM for Cell Broadband Engine Petr Hafner (hafnep1@fel.cvut.cz) SOM self organizing maps About Illustration a subtype of artificial neural networks unsupervised learning vector quantization
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Application
- Speech, audio, video … processing
- Face identification system
- Hand-writing text transcription
- Searching similarities in an unknown
signals
- Unknown noise reduction
- Automatic sorting
IBM - CVUT Student Research Projects 3
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Principle
- Randomize the map's nodes' weight vectors
- Grab an input vector
- Traverse each node in the map
– Use Euclidean distance formula to find similarity between the input vector and the map's node's weight vector – Track the node that produces the smallest distance (so-called Best Matching Unit – BMU)
- Update the nodes in the neighbourhood of BMU
– Wv(t + 1) = Wv(t) + Θ(t)α(t)(D(t) - Wv(t))
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Goals of the project
- To study the
architecture of the Cell Broadband Engine
- To create effective
vector implementation
- f the SOM on the
Cell
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What’s done?
- Understanding and orientation in Cell
Broadband Engine architecture
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What is to be done?
- Multithreaded solution
- Vector multithreaded solution
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