I want to implement the example of letter 'A' on the wiki page of nonlinear dimensionality reduction(https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction). I use python to generate the data, and then use waffles to do dimensionality reduction. However, the result of PCA is a cicle while the result of manifold sculpting is some disordered points. Could someone help me with this problem?
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
I did that a long time ago, and have since forgotten some of the details. However I found this in a search of my computer: http://uaf46365.ddns.uark.edu/letter.zip
It may or may not be the data that produced that figure. The file log.txt appears to contain some notes about the exact commands I used to produce varying quality of results. Apparently, it took some experimentation before I was happy with the results. I am not sure whether the best one is in there or not.
(Tangentially, if you're looking for an NLDR algorithm that just works in a variety of cases, frankly, I'd recommend looking into some of the neural network-based feature-learning methods. Manifold Sculpting can produce good results in specific cases, but it requires careful tuning and a lot of processing time. Ultimately, all of the NLDR methods that require neighbor-finding as their first step end up being too slow for general applications, too susceptible to problems with poorly-sampled manifolds, and unable to generalize effectively for out-of-band samples. The neural network approaches, by contrast, require no neighbor-finding step, and are designed to generalize.)
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
View and moderate all "Help" comments posted by this user
Mark all as spam, and block user from posting to "Discussion"
I want to implement the example of letter 'A' on the wiki page of nonlinear dimensionality reduction(https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction). I use python to generate the data, and then use waffles to do dimensionality reduction. However, the result of PCA is a cicle while the result of manifold sculpting is some disordered points. Could someone help me with this problem?
I did that a long time ago, and have since forgotten some of the details. However I found this in a search of my computer: http://uaf46365.ddns.uark.edu/letter.zip
It may or may not be the data that produced that figure. The file log.txt appears to contain some notes about the exact commands I used to produce varying quality of results. Apparently, it took some experimentation before I was happy with the results. I am not sure whether the best one is in there or not.
(Tangentially, if you're looking for an NLDR algorithm that just works in a variety of cases, frankly, I'd recommend looking into some of the neural network-based feature-learning methods. Manifold Sculpting can produce good results in specific cases, but it requires careful tuning and a lot of processing time. Ultimately, all of the NLDR methods that require neighbor-finding as their first step end up being too slow for general applications, too susceptible to problems with poorly-sampled manifolds, and unable to generalize effectively for out-of-band samples. The neural network approaches, by contrast, require no neighbor-finding step, and are designed to generalize.)