Unsupervised Learning

Unsupervised learning is very similar to supervised learning in that it takes certain inputs, images for example, and can apply labels to that input. Supervised learning, by contrast, follows by example. Supervised learning requires a human to show the program a bunch of images and categorize them for the program. The human must specify what is in each photograph. The program will then be able to use its previously learned images to infer labels onto new images it sees.

Unsupervised learning differs from supervised learning in that it doesn’t require previous inputs to be categorized or labeled. Unsupervised learning can take an image and analyze it without any previous examples. For instance, our current programming assignment is to take a given image and analyze it to find lines. The program didn’t have any previous examples of lines in images to look for. The program instead uses the RANSAC algorithm and keeps finding lines in the image until no more lines can be found. This can be extended to full resolution and full color images. The program can be told to cluster the image into similar sections and colors. This can help the program find edges of objects in the pictures. These edges can help create wire meshes for the images or can slice the image off into different sections.

Unsupervised learning can also be used with text instead of images. A program could look at a corpus of text and sort that data into certain groups for clustering. For example, a program could try to categorize a small corpus of posts on a blogging site. To follow the RANSAC algorithm, the program could randomly pick a few words from random posts and find inliers for those words (blogs with similar words). The program would then continue to refit the model to the new inliers it picked up until the model stops changing. This would create clusters of different categories throughout the site and would help people browse categories they are interested in.

Yahoo does something similar to this where it tried indexing the whole web. Yahoo is primarily used to input search queries and find specifically what you are looking for on the web, but they also have the option to choose a category that they have predefined. You could then explore the web sites that they have in this category. They can create this organization in a similar fashion to the blogging site example. They create a narrow topic and then find all sites that fall into this model using unsupervised learning. These are only a few of the many examples for uses of unsupervised learning.