Unsupervised feature extraction based on uncorrelated approach

• Published in: Information sciences Vol. 666; p. 120447
• Main Authors: Jayashree, Shiva Prakash, T., Venugopal, K.R.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.05.2024
• Subjects: Clustering; Correlation; Dimensionality-reduction; Feature engineering; Feature extraction; Feature selection; Single views; Uncorrelated features; Correlation; Feature selection; Uncorrelated features; Dimensionality-reduction; Feature extraction; Single views; Clustering; Feature engineering
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2024.120447

In high-dimensional spaces, mathematically driven data processing methods have recently attracted a lot of attention. We consider the situation when information is obtained by sampling a probability distribution with support on or close to a sub-manifold of Euclidean space. In this paper, we provide an innovative unsupervised learning method called Uncorrelated Neighborhood Preserving Embedding (UNPE) which identifies the underlying manifold structure of a data set and preserves the neighborhood structure of the data set. We provide a concrete formulation with UNPE, an iterative technique to demonstrate the usefulness of the framework, which has been confirmed by experimental findings on datasets Coil20, Pie, Tox, and Prostate-GE that uses three different parameters viz., F-score, NMI, and accuracy. It is observed that performance is better than the LPP algorithm by 1%, PCA by 2%, and more than 2% of LLE and LE algorithms.