A survey of dimensionality reduction techniques

By Carlos Oscar Sánchez Sorzano, Javier Vargas, Alberto Domingo Pascual-Montano. arXiv.org, 2014

443 citations recorded by the source corpus.

Experimental life sciences like biology or chemistry have seen in the recent decades an explosion of the data available from experiments. Laboratory instruments become more and more complex and report hundreds or thousands measurements for a single experiment and therefore the statistical methods face challenging tasks when dealing with such high dimensional data. However, much of the data is highly redundant and can be efficiently brought down to a much smaller number of variables without a significant loss of information. The mathematical procedures making possible this reduction are called dimensionality reduction techniques; they have widely been developed by fields like Statistics or Machine Learning, and are currently a hot research topic. In this review we categorize the plethora of dimension reduction techniques available and give the mathematical insight behind them.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9165ff19dcfede7ad02dd231804445f55f90658e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Alexandru Cristian Telea
2. Nina Sumiko Tomita Hirata
3. Mateus Espadoto

In Computer Vision and Pattern Recognition

1. N. Doulamis
2. Alexandru Cristian Telea
3. Philip Branch
4. Anastasios D. Doulamis
5. Eftychios E. Protopapadakis
6. Andreas Kerren
7. Nina Sumiko Tomita Hirata
8. Rafael Messias Martins

Citations in the corpus

• Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery (2021, methodology): “Regarding information redundancy reduction many methods can be applied such as vector quantization and mixture models, Principal Component Analysis (PCA), Singular Value Decomposition (SVD), etc. [6].”
• Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE) (2021, unknown): “Issues, 9, 3, 200 (2012) [25] Sorzano, C.”
• A comprehensive survey of anomaly detection techniques for high dimensional big data (2020, background): “Sorzano et al. [14] categorize dimensionality reduction techniques, along with the underpinning mathematical insights.”
• Toward a Quantitative Survey of Dimension Reduction Techniques (2019, methodology): “We see that some surveys include many techniques, but discuss these from a technical/mathematical viewpoint rather than a practical one [23], or have a more educational, rather than evaluational, purpose [12].”