LIII. On lines and planes of closest fit to systems of points in space

By Karl Pearson F.R.S.. 1901

Subfields: Representation & Self-Supervised Learning

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Citations in the corpus

Hierarchical Text-Conditional Image Generation with CLIP Latents (2022, methodology): “After applying PCA, we order the principal components by decreasing eigenvalue magnitude, quantize each of the 319 dimensions into 1,024 discrete buckets, and predict the resulting sequence using a decoder-only Transformer [53] model with a causal attention mask.”
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, methodology): “Early approaches toward this problem, including Principal Component Analysis (PCA) (Pearson, 1901) and Multidimensional Scaling (MDS) (Torgerson, 1952), mostly fall into the global category.”
Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “Many classical learning algorithms—such as the perceptron (Rosenblatt 1958), support vector machine (SVM) (Cortes and Vapnik 1995), and principal component analysis (PCA) (Pearson 1901, Hotelling 1933)—employ data instances, e.g., x,x′ ∈ X , only through an inner product 〈x,x′〉, which basically is a”
The Price of Fair PCA: One Extra Dimension (2018, methodology): “We focus on principal component analysis (henceforth PCA), perhaps the most fundamental dimensionality reduction technique in the sciences [Pearson, 1901; Hotelling, 1933; Jolliffe, 1986].”
The Ideal Continual Learner: An Agent That Never Forgets (2023, methodology): “PCA refers to modeling a data matrix [Y1 · · · YT ] with some low-dimensional subspace, and it is a century-old topic (Pearson, 1901) that has many applications and extensions (Jolliffe, 2002; Vidal et al.”
Geometric median and robust estimation in Banach spaces (2013, background): “It is well known that classical Principal Component Analysis (PCA) [38] is very sensitive to the presence of the outliers in a sample.”
Principal Component Analysis (2003, neutral): “532 (see first coefficient of PC1 in (1)).”
Revisiting the Determinants of Productivity Growth: What's New? (2009, methodology): “Each component is interpreted using the contributions of variables to the component (Pearson, 1901 and Jolliffe, 2002).”