Feature dimensionality reduction: a review

Abstract As basic research, it has also received increasing attention from people that the “curse of dimensionality” will lead to increase cost data storage and computing; influences efficiency accuracy dealing with problems. Feature dimensionality reduction as a key link in process pattern recognition become one hot difficulty spot field recognition, machine learning mining. It is most challenging research fields, which been favored by scholars’ attention. How implement “low loss” feature dimension reduction, keep nature original data, find out best mapping get optimal low dimensional are keys aims research. In this paper, two-dimensionality methods, selection extraction, introduced; current mainstream algorithms analyzed, including method for small sample based on deep learning. For each algorithm, examples their application given advantages disadvantages these methods evaluated.