Helping computers learn to tackle big-data problems outside their comfort zones
Imagine combing through thousands of mugshots desperately looking for a match. If time is of the essence, the faster you can do this, the better. A*STAR researchers have developed a framework that could help computers learn how to process and identify these images both faster and more accurately1.
Peng Xi of the A*STAR Institute for Infocomm Research notes that the framework can be used for numerous applications, including image segmentation, motion segmentation, data clustering, hybrid system identification and image representation.
A conventional way that computers process data is called representation learning. This involves identifying a feature that allows the program to quickly extract relevant information from the dataset and categorize it — a bit like a shortcut. Supervised and unsupervised learning are two of the main methods used in representation learning. Unlike supervised learning, which relies on costly labeling of data prior to processing, unsupervised learning involves grouping or ‘clustering’ data in a similar manner to our brains, explains Peng.