An algorithm research of supervised LLE based on mahalanobis distance and extreme learning machine
Lingmin He, Wei Jin, Yang Xiao-bin, Kangjian Wang · 2013
The Locally Linear Embedding (LLE) is one of the efficient nonlinear dimensionality reduction techniques. But for some high dimensional data, it is not taking the class information of the data into account and Euclidean distance can not accurately reflect the similarity among samples. The paper proposes an improved Supervised LLE which combines class labeled data and Mahalanobis Distance (MSP-LLE). First, the approach learns a Mahalanobis Distance from the existing data. Then the Mahalanobis Distance and label information are combined to choose neighborhoods. Finally, ELM is using to map the unlabeled data to the feature space, which easily implement fault pattern recognition. The experiment result shows its good performance on reduction and recognition for high-dimensional and similar data.