Unit 6 5i EM as Generalization of k-MeansYouTube↗  IA↗(long shot)
Howto & Style  3.7K views 7 likes 0 dislikes   22 Oct 2011
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It is now really easy to explain expectation maximization
Teraz już łatwo wyjaśnić dlaczego maksymalizacja oczekiwania
as a generalization of K-means.
jest uogólnieniem k-średnich.
Again, we have a couple of data points here
Jak zawsze, mamy kilka punktów
and 2 randomly chosen cluster centers.
oraz dwa losowo wybrane centra grupy.
But in the correspondence step instead of making a hard correspondence
Ale teraz zamiast dopasowywać bezpośrednio do któregoś z centrów
we make a soft correspondence.
będziemy dopasowywać pośrednio.
Each data point is attracted to a cluster center
Każdy punkt jest przyciągany do centrum
in proportion to the posterior likelihood
z siłą proporcjonalną do pewnego prawdopodobieństwa a posteriori,
which we will define in a minute.
które za chwilę zdefiniuję.
In the adjustment step or the maximization step
W kroku dopasowywania, lub maksymalizacji,
the cluster centers are being optimized just like before
centra grup są optymalizowane jak poprzednio
but now the correspondence is a soft variable
ale przypisywanie nie jest już jednoznaczne
and they correspond to all data points in different strengths
dane są przypisywane do wszystkich centrów z różną siłą,
not just the nearest ones.
nie tylko do tych najbliższych.
As a result, in EM the cluster centers
W związku z tym, w maksymalizacji oczekiwania centra grup
tend not to move as far as in K-means.
nie przemieszczają się tak daleko jak w przypadku K-średnich.
Their movement is smooth away.
Ich ruch jest łagodniejszy
A new correspondence over here gives us different strength
Nowe dopasowanie daje różne siły,
as indicated by the different coloring of the links
oznaczone tu różnymi kolorami,
and another relaxation step gives us better cluster centers.
a kolejne kroki relaksacji dają lepsze centra grup.
And as you can see over time, gradually
Z upływem czasu, jak widzicie, stopniowo
the EM will then converge to about the same solution as K-means.
MO będzie zbiegał mniej więcej do takiego samego rozwiązania jak w przypadku K-średnich.
However, all the correspondences are still alive.
Wszystkie przypisania są jednak wciąż aktywne.
Which means there is not a 0, 1 correspondence.
Nie ma przypisania zero - jedynkowego.
There is a soft correspondence
Jest ono pośrednie,
which relates to a posterior probability, which I will explain next.
zależne od prawdopodobieństwa a posteriori, które zaraz wyjaśnię.
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