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The only parameter to be adjusted on this stage is τ defined as:
min
j
s j )
d (( x i ,y i ) ,
.
τ =max
i
(1)
where d (( x i ,y i ) ,
s j ) is the distance between point ( x i ,y i ) of the original
trajectory and the segment
s j
of the linearized trajectory.
2.2 Growing Neural Gas
Once we have extracted all the segments from the observed set of trajectories,
the GNG algorithm is used to find clusters of sub-trajectories. The data used
in the training process is the whole set of segments, from all the trajectories,
found in the previous stage. This training set is defined as
S
{ s 1 ,
s 2 , ...
s m }
,
where m is the total number of segments. It is important to highlight that, in
order to find the clusters, the information about what trajectory is producing
the segment s i is not present. The procedure can be summarized in the following
steps:
=
1. Define a maximum number of iterations, mxiter ,amaximumnumberof
nodes mxnodes , the actual number of nodes n = 0, a matrix of weights
Ω
, a vector of errors, E = 0, a matrix of connections, C = 0, and set
the actual iteration iter =1.
2. Randomly select two segments from the training set, (
=
s a ,
s b ) and assign them
as the weights of the two first nodes:
ω 1 s a
ω 2 s b
Ω
.
(2)
=
{ ω 1 ,
ω 2 }
Set a connection C (1 , 2) = C (2 , 1) = 1 and make n =2.
3. Randomly select a segment,
s i
from the training set and search the near-
est weight,
ω s . Connect nodes f and s , C ( f, s )=
C ( s, f ) = 1. Increment the error associated to node f by:
ω f
and the second one
2
E f
= E f +
s i
ω f
(3)
Update the reference vectors of the winner and its direct topological neigh-
bors by fractions ε a
and ε n , respectively, of the total distance to the input
vector:
Δ
ω f
= ε a (
s i
ω f ); Δ
ω j = ε b (
s i
ω j ) j
N f
(4)
N f is the set of direct topological neighbors of node f .
4. Increment the connection value of all neighbors of f , C ( f, j )= C ( f, j )+
1 ,C ( j, f )= C ( j, f ) + 1. Remove all the connections, C ( f, h )= C ( h, f )=0,
greater than a predefined value α . If in this step a node is found to be isolated,
it is removed from all associated vectors and matrices and the number of
active nodes is decreased. Increment the value of iter .
where
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