Deep Learning (DL) has achieved remarkable success in tackling complex Artificial
Intelligence tasks. The standard training of neural networks employs backpropagation to …

Common subspace learning methods only utilize local or global structure in feature
extraction, and cannot obtain the global optimal discriminative projection matrix. For this …

Abstract This paper introduces Multiple Manifold Locally Linear Embedding (MM-LLE)
learning. This method learns multiple manifolds corresponding to multiple classes in a data …

Existing data-driven bridge deterioration prediction methods mostly learn from abstract
inventory data from a single source to predict the future conditions of bridges. Bridge …

Machine learning deals with datasets characterized by high dimensionality. However, in
many cases, the intrinsic dimensionality of the datasets is surprisingly low. For example, the …

The intrinsic nature of noisy and complex data sets is often concealed in low-dimensional
structures embedded in a higher dimensional space. Number of methodologies have been …

In practical applications, the data set we deal with is typically high dimensional, which not
only affects training speed but also makes it difficult for people to analyze and understand. It …

Geodesic distance, as an essential measurement for data similarity, has been successfully
used in manifold learning. However, many geodesic based isometric manifold learning …

This paper evaluates the performance of different feature extraction or dimensionality
reduction techniques for the applications of short-term power forecasting using smart meters' …

Unsupervised classification is a crucial step in remote sensing hyperspectral image analysis
where producing training labelled data is a laborious task. Hyperspectral imagery is …