Overview and comparative study of dimensionality reduction techniques for high dimensional data
By Shaeela Ayesha, Muhammad Kashif Hanif, Ramzan Talib. Information Fusion, 2020
505 citations recorded by the source corpus.
Abstract The recent developments in the modern data collection tools, techniques, and storage capabilities are leading towards huge volume of data. The dimensions of data indicate the number of features that have been measured for each observation. It has become a challenging task to analyze high dimensional data. Different dimensionality reduction techniques are available in literature to eliminate irrelevant and redundant features. Selection of an appropriate dimension reduction technique can help to enhance the processing speed and reduce the time and effort required to extract valuable information. This paper presents the state-of-the art dimensionality reduction techniques and their suitability for different types of data and application areas. Furthermore, the issues of dimensionality reduction techniques have been highlighted that can affect the accuracy and relevance of results.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/61980c1e1085a1d80ecf350ba14d6de5c56ce939
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Arman Malekloo
- Mark A. Girolami
- Ekin Ozer
- Mohammad AlHamaydeh
- Francesco Camastra
- Ivor Wai-Hung Tsang
- Brian Kulis
- Inderjit S. Dhillon
In Media Technology
Citations in the corpus
- A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction (2020, background): “However, in feature extraction, the dimension can be decreased without losing much initial feature dataset [2, 10, 14, 17].”
- Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, background): “Interested readers are referred to the recent comparative study of dimensionality reduction techniques by Ayesha et al. 107 for more detail.”