Survey on dimension reduction techniques

By Minghe Xu. 2006

1,156 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/099b54ffe20b1a50b007b4e7b947087a73b021c8

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Citations in the corpus

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks (2017, methodology): “For more on dimensionality reduction techniques, please refer to [242]–[244].”
Review of Input Variable Selection Methods for Artificial Neural Networks (2011, methodology): “Essentially, PCA can be considered a data pre-processing algorithm that determines an optimal rotational transformation of the dataset, X, that maximises the amount of variance of the output Y that is explained by the PCs (Fodor, 2002).”
Active learning from demonstration for robust autonomous navigation (2012, background): “Dimensionality reduction is itself another vast area of research [13], with Principal Components Analysis (PCA) being perhaps the single most common approach.”
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, methodology): “There exist different techniques for reducing data dimensions [138] including projection of higher dimensional points”
Clustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications (2016, methodology): “Feature extraction which transforms the data in the high-dimensional space into a space of fewer dimensions [17], is often used to reduce the scale of the input data.”
A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems (2015, unknown): “Dimensionality reduction is a commonly used step in machine learning, especially when dealing with a high dimensional space of features (Fodor, 2002; Van der Maaten, Postma, & van den Herik, 2008).”
Stratified Transfer Learning for Cross-domain Activity Recognition (2017, methodology): “PCA and KPCA are classic dimensionality reduction meth-ods, while TCA, GFK, and TKL are representative transfer learning approaches.”
Principal Component Analysis on Spatial Data: An Overview (2013, unknown): “Often it is used as a preprocessing method either for data orthogonalization and eliminating redundancy caused by variable correlation or for dimensionality reduction, before employing another statistical method, such as regression or clustering (Fodor 2002; Jolliffe 2002).”
Challenges of Big Data Analysis (2013, unknown): “Accordingly, to effectively handle Big Data in both statistical and computational perspectives, dimension reduction as an important data pre-processing step is advocated and exploited in many applications [95].”
Toward a Quantitative Survey of Dimension Reduction Techniques (2019, background): “Fodor [18] presents the earliest survey on projection techniques that we are aware of, which includes what are now considered modern methods, i.e., nonlinear methods, vector quantization, and neural networks.”
A survey of dimensionality reduction techniques (2014, background): “…computational resources have attracted the attention of many researchers in Statistics, Computer Science and Applied Mathematics who have developed a wide range of computational techniques dealing with the dimensionality reduction problem (for reviews see [Carreira1997, Fodor2002,Mateen2009]).”
Dimension Reduction for Aerodynamic Design Optimization (2011, background): “Fodor [11] and van der Maaten et al.”
A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm (2013, methodology): “To further improve the imputation accuracy, feature selection and/or dimension reduction techniques [13] can be applied to the training data; for example, principal component analysis methods can be applied before the support vector training to reduce the training time.”
Fog Computing for Sustainable Smart Cities (2017, methodology): “Dimension reduction techniques can be used to reduce the amount data that need to be communicated to the cloud for further analysis [Fodor 2002].”