PCA, SVD, LDA Linear Dimensionality Reduction Techniques

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DecisionForest

Hi sir, thank you very much for the video! I have experimental data set of time dependent signals with same integer output like 500 watt, input is 800(time)x49(different signals) voltage values. I used PCA and reduced it to 800x2. How can I reduce further and extract information from these set for ML application and is there any other feature extraction method that you can advice for signal feature extraction ?

hakanbayer

Hi sir, thank you very much for the video! I have experimental data set of time dependent signals with same output of 500 watt and 800(time)x49(signals) input voltage values. I used PCA and reduced it to 800x2 .can I reduce further and extract information from these set for ML application and is there any other feature extraction method that you can advice for signal feature extraction ?

hakanbayer

Is word2vec using dimensional reduction too?

lemoniall

Hi for PCA, ı guess you need to normalize the data first. I have not seen the normalization-standardiztion procedure in the code

MrCaglar

You've set the number of components to 20, maybe that was the reason PCA didn't work

spark-to-a-flame

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