how can make a process after dimension reduction like ICA ?

norah
norah New Altair Community Member
edited November 5 in Community Q&A
hello,
I want to ask about dimension reduction I used ICA on dataset about climate it has (temperature degree, pressure and so on) so after reducing dimension I get attribute with name IC1, IC2 and so on.
now I want to make some operations on it but I confused how can I make a process like summation if I want on temperature even I don't temperature attribute name now.
depend on what can I make the process after dimension reduction?


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Best Answers

  • varunm1
    varunm1 New Altair Community Member
    Answer ✓
    I will explain to you a famous use case. Blind source separation techniques like ICA are used for finding hidden variables.

    Example: Imagine that there are four people in a room talking at a time with each other and you have two microphones that record their audio data. Now you have two audio signals that consist of all 4 people voices in each one of them but it is difficult for you to identify people based on audio data (mixed). Now what ICA does is it takes different features from audio signals and create four independent components so that each belongs to a particular person. The main use of ICA technique is to find independent hidden variables. 

    Based on the math behind this algorithm we can use it in dimensionality reduction.

    Thanks 

Answers

  • norah
    norah New Altair Community Member
    ok, what's the benefit from this feature I mean dimension reduction except for low space and time consuming if I can not know the original attributes? can you give me an example of using this 
  • varunm1
    varunm1 New Altair Community Member
    Answer ✓
    I will explain to you a famous use case. Blind source separation techniques like ICA are used for finding hidden variables.

    Example: Imagine that there are four people in a room talking at a time with each other and you have two microphones that record their audio data. Now you have two audio signals that consist of all 4 people voices in each one of them but it is difficult for you to identify people based on audio data (mixed). Now what ICA does is it takes different features from audio signals and create four independent components so that each belongs to a particular person. The main use of ICA technique is to find independent hidden variables. 

    Based on the math behind this algorithm we can use it in dimensionality reduction.

    Thanks 
  • norah
    norah New Altair Community Member
    I got it, thank you so much ..