unsupervised-learning

In supervised learning I have the typical train/test split to learn the algorithm, e.g. Regression or Classification. Regarding unsupervised learning, my question is: Is train/test split necessary and useful? If yes, why? Well This Depend on the Problem, the form of dataset and Class of Unsupervised algorithm used to solve the particular problem. Roughly:- Dimensionality reduction techniques are usually tested by calculating the error in reconstruction so there we can use k-fold cross-validation procedure But on clustering algorithm, I would suggest doing statistical testing in order to test

Given a data frame containing mixed variables (i.e. both categorical and continuous) like, digits = 0:9 # set seed for reproducibility set.seed(17) # function to create random string createRandString <- function(n = 5000) { a <- do.call(paste0, replicate(5, sample(LETTERS, n, TRUE), FALSE)) paste0(a, sprintf("%04d", sample(9999, n, TRUE)), sample(LETTERS, n, TRUE)) } df <- data.frame(ID=c(1:10), name=sample(letters[1:10]), studLoc=sample(createRandString(10)), finalmark=sample(c(0:100),10), subj1mark=sample(c(0:100),10),subj2mark=sample(c(0:100),10) ) I perform unsupervised feature selection

I need to perform clustering without knowing in advance the number of clusters. The number of cluster may be from 1 to 5, since I may find cases where all the samples belong to the same instance, or to a limited number of group. I thought affinity propagation could be my choice, since I could control the number of clusters by setting the preference parameter. However, if I have a single cluster artificially generated and I set preference to the minimal euclidean distance among nodes (to minimize number of clusters), I get terrible over clustering. """ ==========================================

I'm trying to implement a face recognition algorithm using Python. I want to be able to receive a directory of images, and compute pair-wise distances between them, when short distances should hopefully correspond to the images belonging to the same person. The ultimate goal is to cluster images and perform some basic face identification tasks (unsupervised learning). Because of the unsupervised setting, my approach to the problem is to calculate a "face signature" (a vector in R^d for some int d) and then figure out a metric in which two faces belonging to the same person will indeed have a