Artificial Neural Networksvasighi/courses/ann97win/ann12.pdfSelf Organizing Maps Topology The...
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Artificial Neural Networks Part 12
Transcript of Artificial Neural Networksvasighi/courses/ann97win/ann12.pdfSelf Organizing Maps Topology The...
Artificial Neural
Networks
Part 12
Self Organizing Maps Introduction
Teuvo Kohonen has introduced several new
concepts to neural computing.
The most popular one is the self-organizing
feature maps (SOMs) which can be used for
visualization and clustering of high dimensional
data.
He is a Professor of the Academy of Finland
The Kohonen ANN is a ‘self-organizing’ system which is capable to
solve the unsupervised rather than the supervised problems.
Self Organizing Maps Introduction
Grandmother cell theory:A "grandmother cell" is a hypothetical
neuron that responds only to a specific
and meaningful stimulus, such as the
image of one's grandmother. The term
originated by Jerry Lettvin in 1967.
o The neurons are organized according to a physical network of
connections in the brain (planar topology).
o Some neurons are tuned by evolution and training to fire
electrical signals for particular events.
o Neurons that are neighbors tend to fire for similar input data.
Self Organizing Maps Introduction
Self Organizing Maps Introduction
Self Organizing Maps Structure
As a rule, the Kohonen type of net is based on a single layer of neurons
arranged in a two-dimensional plane having a well defined topology
A defined topology means that each neuron has a defined number of
neurons as nearest neighbors, second-nearest neighbor, etc.
Two-dimensional lattice of neurons, illustrated for a three-dimensional
input and four-by-four dimensional output
Self Organizing Maps Structure
Self Organizing Maps Topology
The neighborhood of a neuron is usually arranged either in squares or
in hexagon.
In the Kohonen conception of neural networks, the signal similarity is
related to the spatial (topological) relation among neurons in the
network.
Self Organizing Maps Learning scheme
Similarity is the basis of
selection of the winner
neuron.
In other words, there is a
competition between neurons
for winning.
(competitive learning)
The Kohonen learning concept tries to map the input so that similar signals excite neurons that are very close together.
W
Input vector
Output
Weight vector
w
xs
Similarity map
Self Organizing Maps Training
cout
2
jisi
m
1j
wxmin
1st step : an m-dimensional
object xs enters the network and
only one neuron from those in
the output layer is selected after
input occurs, the network
selects the winner “c” (central)
according to some criteria.
To begin, we assign random
numbers to each of the weights, as
in the case of other Neural
Network computations
2nd step : After finding the neuron c, its weight vector are corrected to
make its response closer to input.
old
jiSi
old
ji
new
ji wXtjcdtww ),()(
min
max
maxminmax
1)()( at
ttaat
W
Self Organizing Maps Training
amax
dc d
Triangular
amax
dc d
Mexican
hat
3rd step : The weight of neighboring neurons must be
corrected as well. These corrections are usually
scaled down, depending on the distance from c.
old
jiSi
old
ji
new
ji wXtjcdtww ),()(
4th step : The next object Xs is input and the
process repeated. After all objects are input
once, one epoch is completed.
Self Organizing Maps Training
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4×4 map
1.0
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Input vector
output
0.34 0.80 0.52 0.76
1.28 0.46 0.80 1.18
0.82 0.30 0.76 0.44
1.06 0.32 1.18 1.16
cout
m
1i
sijij xwmaxoutmax
Winner
Self Organizing Maps Training
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Input vector
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1× 0.9×
0.8×0.9×
0.6×0.9×
× 0.4×0.9
old
jiSiji wXtjcdtw ),()(
min
max
maxminmax
1)()( at
ttaat
amax=0.9
amin=0.1 max)1( at=1 (first epoch)
Neighbor function: Linearold
jiSi wX
win
ner
old
jiw
d
Self Organizing Maps Training
Initialize Network
Get Input
Find Winner
Update Winner
Update Neighborhood
Repeat for
all input objects
Repeat n time
epochs
Self Organizing Maps Training
After the training process accomplished, the complete set of the training
vectors is once more run through the KANN. In this last run the labeling of
the neurons excited by the input vector is made into the table called top
map.
e
d b
c
a
Top Map
a
Input vectors
b c d e
Trained SOM
Self Organizing Maps Top Map
The number of weights in each neuron is equal to the dimension m of the
input vector. Hence, in each level of weight only data of one specific
variable are handled.
Trained KANN
0 0 0 0 0
1 0 0 0 0
1 1 0 0 0
4 3 1 1 0
5 6 2 1 1
1 3 0 1 2
3 2 2 1 3
2 1 1 2 3
1 2 1 0 1
3 2 1 1 2XS
Input Vector
L L L L
L L L
H
H H H H
H H H
Top Map
Self Organizing Maps Weight Map
The U-matrix is simply a collection of pairwise distances between the
model vectors of neighboring SOM. With it, the clusters can be
visualized as gray shades on top of the SOM display.
U-matrix25×25
Long distances correspond to dark
shades and short distances to light
shades.
Self Organizing Maps U-matrix
Self Organizing Maps Bounding
Kohonen Map
toroid
W
3rd layer of neighbor neurons
Self Organizing Maps Inside feature space
Suppose we use a 3×3 SOM network for training our data set.Our example data has 10 points (vector) in two dimensions.(size of data matrix is 2×10)
x1 x2 x3 …
y1 y2 y3 …
Self Organizing Maps Inside feature space
x1 x2 x3 …
y1 y2 y3 …
3×3 SOM
Suppose we use a 3×3 SOM network for training our data set.Our example data has 10 points (vector) in two dimensions.(size of data matrix is 2×10)
Self Organizing Maps Inside feature space
Inputsamplex1 x2 x3 …
y1 y2 y3 …
3×3 SOM
Suppose we use a 3×3 SOM network for training our data set.Our example data has 10 points (vector) in two dimensions.(size of data matrix is 2×10)
Self Organizing Maps Inside feature space
x1 x2 x3 …
y1 y2 y3 …
3×3 SOM
Suppose we use a 3×3 SOM network for training our data set.Our example data has 10 points (vector) in two dimensions.(size of data matrix is 2×10)
Self Organizing Maps Inside feature space
x1 x2 x3 …
y1 y2 y3 …
3×3 SOM
Suppose we use a 3×3 SOM network for training our data set.Our example data has 10 points (vector) in two dimensions.(size of data matrix is 2×10)
Self Organizing Maps Inside feature space
x1 x2 x3 …
y1 y2 y3 …
3×3 SOM
Suppose we use a 3×3 SOM network for training our data set.Our example data has 10 points (vector) in two dimensions.(size of data matrix is 2×10)
Self Organizing Maps Inside feature space
x1 x2 x3 …
y1 y2 y3 …
3×3 SOM
Suppose we use a 3×3 SOM network for training our data set.Our example data has 10 points (vector) in two dimensions.(size of data matrix is 2×10)
Self Organizing Maps Inside feature space
x1 x2 x3 …
y1 y2 y3 …
3×3 SOM
Suppose we use a 3×3 SOM network for training our data set.Our example data has 10 points (vector) in two dimensions.(size of data matrix is 2×10)
Self Organizing Maps Inside feature space
x1 x2 x3 …
y1 y2 y3 …
3×3 SOM
Suppose we use a 3×3 SOM network for training our data set.Our example data has 10 points (vector) in two dimensions.(size of data matrix is 2×10)
Self Organizing Maps Inside feature space
x1 x2 x3 …
y1 y2 y3 …
3×3 SOM
Suppose we use a 3×3 SOM network for training our data set.Our example data has 10 points (vector) in two dimensions.(size of data matrix is 2×10)
Self Organizing Maps Inside feature space
x1 x2 x3 …
y1 y2 y3 …
3×3 SOM
Suppose we use a 3×3 SOM network for training our data set.Our example data has 10 points (vector) in two dimensions.(size of data matrix is 2×10)
Self Organizing Maps Inside feature space
x1 x2 x3 …
y1 y2 y3 …
3×3 SOM
Suppose we use a 3×3 SOM network for training our data set.Our example data has 10 points (vector) in two dimensions.(size of data matrix is 2×10)
Self Organizing Maps Inside feature space
x1 x2 x3 …
y1 y2 y3 …
3×3 SOM
Suppose we use a 3×3 SOM network for training our data set.Our example data has 10 points (vector) in two dimensions.(size of data matrix is 2×10)
Self Organizing Maps Inside feature space
http://www.peltarion.com/doc/images/Animated_SOM_operation.gif
http://red.csie.ntu.edu.tw/demo/art/CSM/img/SOM_2D.jpg
Self Organizing Maps Inside feature space
