Hi all!
I'm currently exploring alternatives to PCA, CVA, and CMD for data visualization. A colleague introduced me to t-SNE (t-Distributed Stochastic Neighbor Embedding, Van der Maaten & Hinton, 2008), which is a method used to reduce high-dimensional data. This is the first time I've come across this method, and I haven't seen it mentioned in many "theoretical" articles on geometric morphometrics (except for Courtenay, 2022). I would appreciate hearing your opinion on the application of t-SNE in geometric morphometrics. What are its advantages, disadvantages, and potential uses? Thank you so much for your input, and I apologize for asking such an open-ended question. Van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. *Journal of machine learning research*, *9*(11). Courtenay, L. A. (2023). Can we restore balance to geometric morphometrics? A theoretical evaluation of how sample imbalance conditions ordination and classification. *Evolutionary Biology*, *50*(1), 90-110. -- You received this message because you are subscribed to the Google Groups "Morphmet" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/morphmet2/22c4eb10-d50a-419f-91ff-d5b295c7ef5en%40googlegroups.com.
