Unsupervised feature extraction based on uncorrelated approach

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...

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Bibliographic Details
Published in:Information sciences 2024-05, Vol.666, p.120447, Article 120447
Main Authors: Jayashree, Shiva Prakash, T., Venugopal, K.R.
Format: Article
Language:English
Subjects:
Clustering
Correlation
Dimensionality-reduction
Feature engineering
Feature extraction
Feature selection
Single views
Uncorrelated features
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Summary: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.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2024.120447