Hi Avi, Thank you for the articles you share. I have a question related to Kernel PCA. Please correct me if my understanding is correct:
For non-linear data in dimension d, to apply PCA, we need to project that data to an even higher dimension d’ to make it linear. Once the data is linear, we use our PCA to reduce the dimensionality from d’ to a dimension less than d.
Hi Avi, Thank you for the articles you share. I have a question related to Kernel PCA. Please correct me if my understanding is correct:
For non-linear data in dimension d, to apply PCA, we need to project that data to an even higher dimension d’ to make it linear. Once the data is linear, we use our PCA to reduce the dimensionality from d’ to a dimension less than d.
That is correct. To put it another way, you increase the dimensionality so that you can decrease it.
Thank you