An algorithm research of supervised LLE based on mahalanobis distance and extreme learning machine

Abstract

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.

Locally Linear Embedding based on Rank-order Distance

“…Mahalanobis distance measurement based locally linear embedding algorithm (MLLE) utilizes Mahalanobis metric to choose neighborhoods (Zhang, 2012). Supervised LLE based on Mahalanobis distance (MSP-LLE) combines class labeled data and Mahalanobis distance to choose neighborhoods and use extreme learning machine to map the unlabeled data to the feature space (He, 2013).…”