Comments for Computer vision for dummies

Comment on What are eigenvectors and eigenvalues? by zh

zh — Fri, 21 Jul 2017 05:50:18 +0000

Great work

Comment on The Curse of Dimensionality in classification by Sara

Sara — Mon, 03 Jul 2017 05:57:37 +0000

Hi, I am a student who follows data analytics course module I my university. I was so confused with all the theories and awful explanations by the lecturer. Thank god I found this article!

Comment on Why divide the sample variance by N-1? by Vincent Spruyt

Vincent Spruyt — Wed, 21 Jun 2017 05:34:04 +0000

Hi Michael, it’s an honor, great work! Thanks a lot!

Comment on Why divide the sample variance by N-1? by Michael Cheng

Michael Cheng — Tue, 20 Jun 2017 11:20:39 +0000

Hi, there, I like your essay very much. I hope you do not mind that I have translated your article to Chinese.
Here is the link: http://commanber.com/2017/06/17/sample-variance/
I will remove my article immediately if you do not allow me to release the Chinese version of your article.
Thanks very much!
Have a good day!

Comment on The Curse of Dimensionality in classification by Kevin George

Kevin George — Thu, 15 Jun 2017 05:31:26 +0000

Hi Vincent, first of all I would like to thank you for the wonder blog post and the work you are doing on visiondummy.

I had one doubt about equation 2. Shouldn’t there be only one distance from a sample point to a centroid? How can there be two distances i.e minimum and maximum distances?

Comment on Feature extraction using PCA by NA

NA — Mon, 12 Jun 2017 16:36:23 +0000

Please write articles describing how LDA, SVD, and ICA work. I like how you are able to take subjects that are very complicated to learn about and teach them in a simple manner at a level I can understand.

Comment on A geometric interpretation of the covariance matrix by NA

NA — Mon, 12 Jun 2017 03:04:46 +0000

I really liked this explanation, thanks.

Comment on How to draw a covariance error ellipse? by Rick Sprague

Rick Sprague — Mon, 12 Jun 2017 02:02:31 +0000

My half minor axis is a negative value and the square root returns a “nan”.

Comment on A geometric interpretation of the covariance matrix by Kamesh

Kamesh — Thu, 08 Jun 2017 21:32:10 +0000

Great write up and explanation.

Consider \Sigma = [2 0.1; 0.1 3]; If you perform an eigenvalue-eigenvector decomposition, i.e. P = VLV^T. The V obtained is no longer a rotation matrix. It is orthogonal but not a rotation matrix. I think Niranjan Kotha sees the same issue. The problem is that the factorization doesn’t always yield a rotation matrix (orthogonal yes, but not the special orthogonal matrix).

Comment on What are eigenvectors and eigenvalues? by Swee Mok

Swee Mok — Tue, 06 Jun 2017 12:40:45 +0000

Great explanation.

It looks like there is a typo in the 2nd line while deriving equation (6). The lambda square should have a positive sign.