A Survey of Dimension Reduction Techniques

By I. Fodor. 2002

314 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/86aa05a4a38d81d95afe31daa6e339237562bd41

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Fog Computing for Sustainable Smart Cities (2017, background): “HTTP: Hypertext Transfer Protocol (HTTP) [Fielding et al. 1999] is an application layer protocol designed for distributed hypertext documents and media.”
Taskonomy: Disentangling Task Transfer Learning (2018, background): “Unsupervised learning is concerned with the redundancies in the input domain and leveraging them for forming compact representations, which are usually agnostic to the downstream task [8, 49, 20, 9, 32, 77].”
Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search (2007, unknown): “Please see [7, 15, 12] for some survey.”
The Intrinsic Dimension of Images and Its Impact on Learning (2021, background): “The idea that real-world data distributions can be described by very few variables underpins machine learning research from manifold learning to dimension reduction (Besold & Spokoiny, 2019; Fodor, 2002).”
Overview and comparative study of dimensionality reduction techniques for high dimensional data (2020, unknown): “Comparative study of DRTs has been presented in [5,11,12,15] but information about new variant of some DRTs has not been presented.”
State Representation Learning for Control: An Overview (2018, methodology): “While this idea is very often exploited with dimensionality reduction algorithms [Fodor, 2002] such as Principal Component Analysis (PCA), we focus here on the recent approaches speciﬁcally dealing with state representation.”
Feature Engineering for Predictive Modeling using Reinforcement Learning (2017, methodology): “Dimensionality reduction methods such as Principal Component Analysis (PCA) and its non-linear variants (Kernel PCA) (Fodor 2002) aim at mapping the input dataset into a lower-dimensional space with fewer features.”
Challenges of Big Data Analysis (2013, unknown): “Accordingly, toeffectivelyhandleBigDatainbothstatisticaland computationalperspectives,dimensionreductionas animportantdatapre-processingstepisadvocated andexploitedinmanyapplications[95].”