Machine Learning | Linear Discriminant Analysis

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Linear Discriminant Analysis (LDA) is a dimensionality reduction technique. As the name implies dimensionality reduction techniques reduce the number of dimensions (i.e. variables) in a dataset while retaining as much information as possible. #MachineLearning #LDA

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perfect explaination! This is the best video on LDA that i watched

wishswiss

Great explaination! This is the best video on LDA that i could find

preethamtsp

Small things matter ... I guess nobody noticed that #theStudyBeast has started using different colored markers to improve his content delivery ... but that has made a positive impact for me at least.

teegnas

Good Explanation. Kindly give video for multiclass classification using LDA

mmurali

i didnt understand the end part where you just neglect that W vector on rhs side. Our objective is to find W so how can u consider it as some constant

ickywitchy

I am coufused with one part when you applied the square formula how did you get the resule w^t[(m0-m1)(m0-m1)^t]w if you can elabotare this point it would be really helpful

shwetamishra

can someone please tell me what exactly is wtx+b is used to find what ?

ayushsingh-qnsb

at 6:58, you are mentioning that we need to take it to higer dimension, can you please explain why we have to take to higher dimension pls .

arakanna

why there is no 1/n in variance formula...can anyone explain?

HITNUT

if I am having 4 classes then what will be the SB (between class variance) equation?

snehalgaikwad

12:55 how did the a^2 +b^2 -2ab come into picture here?

anishmanandhar

Nice video sir. Which book you are using

sangeethak

Is LDA only a feature reduction technique or it does classification of features too??

shilpagupta

Which book you have referred sir. Kindly tell me.

sangeethak

What do you mean by rank one efficient

santanudas

why there is no 1/n in variance formula...can anyone explain?

chinmaygodbole

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