Principal Component Analysis

By Ian T. Jolliffe. 2002

13,002 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computational Mechanics, Geometry and Topology, Representation & Self-Supervised Learning

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Citations in the corpus

Contrastive Multiview Coding (2019, methodology): “of the data that make subsequent problem solving easier [5]. This ﬁeld has a long history, starting with classical methods with well established algorithms, such as principal components analysis (PCA [29]) and independent components analysis (ICA [26]). These methods tend to learn representations th”
Fast Edge Detection Using Structured Forests (2014, methodology): “ such as Gini impurity, may be used (e). injects additional randomness into the learning process and helps ensure a sufﬁcient diversity of trees, see x2.2. Finally, Principal Component Analysis (PCA) [25] can be used to further reduce the dimensionality of Z. PCA denoises Zwhile approximately preser”
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, methodology): “tations are learned from the data by following a sequence of updates to infer the parameters that best match the observed data. Other latent factor methods, such as principal component analysis (PCA) [29] and latent Dirichlet allocation (LDA) [7], similarly frame user and item representations in ter”
Visualizing Large-scale and High-dimensional Data (2016, methodology): “, 2D), the linear methods such as the Principle Component Analysis [15] and the multidimensional scaling [25] usually do not work as effectively as the non-linear methods as most high-dimensional data usually lies on or near low-dimensional non-linear manifolds.”
A survey on data preprocessing for data stream mining: Current status and future directions (2017, background): “So far it is worth mentioning streaming versions f kernel PCA proposed by Joseph et al. [102] and by Ghashami t al.”
Random walk with restart: fast solutions and applications (2008, methodology): “One of the widely used techniques is singular vector decomposition (SVD) [12], which is the base for a lot of powerful tools, such as latent semantic index (LSI) [5], principle component analysis (PCA) [17], and so on.”
Iterative Gaussianization: From ICA to Random Rotations (2011, methodology): “pular approaches rely on linear models and statistical independence. However, they are usually too restrictive to describe general data distributions. For instance, principal component analysis (PCA) [7], that reduces to DCT in many natural signals such as speech, images and video, assumes a Gaussia”
Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art (2008, background): “Principal component analysis [76] [77] refers to a linear transformation of variables that retains -for a given number n of operators the largest amount of variation within the training data.”