Post on 21-Mar-2019
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Zornitsa Kozareva!USC/ISI!
Marina del Rey, CA!kozareva@isi.edu!
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Many low frequency words!
Can we adjust tf?!
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!
d(x, y) = xi " yip
i=1
m
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x = x1,x2,…,xn( )
!
y = y1,y2,…,yn( )
!
d(x, y) = xi " yi2
i=1
m
#2
!
d(x, y) = xi " yii=1
m
#
Euclidean distance
Manhattan distance
(N#8J9"'
``'
!
42 + 32 = 52Euclidean distance
3
4
!
4 + 3= 7Manhattan distance
reminder buying milk !from home to store!
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Slide adapted from CMU
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Task: Cluster the following objects into two clusters (k=2)
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#Manhattan distance Use:
Randomly initialize the clusters with the first two objects C1={(0,0)} C2={(0,1)}
Now: 2. Initialize cluster centers. 3a. Calculate the distance between each object and each cluster center, assigning the object to the closest cluster. 3b. Compute new cluster center for each cluster.
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Feature Representation
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