Multidimensional Scaling: History, Theory, and Applications

By Forrest W. Young. 1987

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Citations in the corpus

Relational autoencoder for feature extraction (2017, background): “Multidimensional Scaling (MDS) [17] considers the relationship of data samples by transforming distances into similarities for visualization.”
Deep Learning with Long Short-Term Memory Networks for Diagnosing Faults in Smart Grids (2021, methodology): “1987), MDS (Young and Hamer 2013), and LDA (Izenman 2013) are employed as dimensionality reduction techniques.”
Survey of clustering algorithms (2005, methodology): “Different from PCA, ICA, and projection pursuit, Multidimensional scaling (MDS) is a nonlinear projection technique [75], [292].”
Efficient Representation of Low-Dimensional Manifolds using Deep Networks (2016, methodology): “A large number of dimensionality reduction techniques, such as principle component analysis (PCA) (Pearson, 1901), multi-dimensional scaling (Young & Hamer, 1987), Isomap (Tenenbaum et al., 2000), and local linear embedding (LLE) (Roweis & Saul, 2000), have been introduced.”
Development and Validation of a Scale for Measuring Instructors' Attitudes toward Concept-Based or Reform-Oriented Teaching of Introductory Statistics in the Health and Behavioral Sciences (2010, methodology): “MDS plots the objects (each item) on a map, placing similar objects close to each other and dissimilar objects further apart (Coxon, 1982; (Kruskal & Wish, 1978; Young, 1987).”
From Visual Data Exploration to Visual Data Mining: A Survey (2003, methodology): “Some common techniques are Principal Component Analysis [2], Factor Analysis [37], Multidimensional Scaling [92], and FastMap [27].”
Learning Distributed Representations of Concepts Using Linear Relational Embedding (2001, background): “Multidimensional Scaling (Kruskal, 1964; Young and Hamer, 1987) ﬁnds a representation of concepts as vectors in a multi-dimensional space, in such a way that the dissimilarity of two concepts is modeled by the Euclidean distance between their vectors.”
Sociological Realms of Emotional Experience1 (2004, methodology): “Viewing emotions as indicators of individuals’ positions in a three-dimensional affective space (e.g., Heise, Smith-Lovin, MacKinnon), the authors find emotional station correlates with a variety of social structural, circumstantial, and individual-level variables.”