Macro Manifold Learning with Applications to Supervised Classification
Hong Bing Huang · Applied Mechanics and Materials · 2014
Manifold learning has made many successful applications in the fields of dimensionality reduction and pattern recognition. However, when it is used for supervised classification, the result is still unsatisfactory. To address this challenge, a novel supervised approach, namely macro manifold learning (MML) is proposed. Based on the proposed approach, the low-dimensional embeddings of the testing samples is more favorable for classification tasks. Experimental results demonstrate the feasibility and effectiveness of our proposed approach.