Ali Ghodsi, Lec 6: Spectral Clustering, Laplacian Eigenmap, MVU

[@ProfessionalTycoons]

Laplacian Eigenmap starts 31:00

[@ProfessionalTycoons]

maximum variance unfolding starts at: 1:02:00

[@micahdelaurentis6551]

his arms are up a lot. Great lectures!

[@TheAhmadob]

How can we find the low rank representation of unfamiliar data in Laplacian Eigenmaps? As in: in PCA you find a transformation matrix that you can apply to any data point you have and it will give you the representation of your data point in the different components. However, it seems in the laplacian eigenmaps that we directly solve for the low rank representation i.e y. Therefore if I have new data points I will have to do everything from scratch? ps: I feel that this has already been discussed in the video but I can't seem to find it again.

[@alirezamogharabi8733]

سلام آقای دکتر قدسی، نمی‌دونم این پیام بنده به شما می‌رسه یا خیر، اما هر بار که ویدیویی از شما می‌بینم از ته دل آرزو میکنم که یه روز بتونم سر کلاس‌های حضرتعالی باشم. لیسانس و فوق لیسانس دانشگاه آزاد خوندم، اما امیدوارم دکترام واترپلو باشه.

[@rbzhang3374]

Why the matrix size is def by the opposite way?

[@ProfessionalTycoons]

very good lecture

[@snehgupta4115]

Hey, I have few doubts can you please clarify ?

[@TheKovosh]

what is A matrix?

[@egebeyazit]

adjcacency

[@VahidOnTheMove]

1:45 Iranian style. Lol

[@VahidOnTheMove]

By the way, it is very well explained!