Sparse multidimensional scaling using land-mark points

By Vin de Silva, Josh Tenenbaum. 2004

325 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1ac1072e320d0e5774006eaab0d636aa97ef58a5

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Citations in the corpus

Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning (2014, unknown): “These methods avoid the computation for the kernel matrix by selecting a subset of the original points, referred to as “landmark samples,” for which the geodesic distance computation is performed and the remainder of the points are inserted into the “backbone,” thereby reducing the computational cos”
Hierarchical Stochastic Neighbor Embedding (2016, methodology): “The complete dataset is then embedded using different interpolation schemes [FFDP15,JPC ∗ 11,PNML08,PSN10, PdRDK99, dST04, PEP ∗ 11].”
Visualizing structure and transitions in high-dimensional biological data (2019, methodology): “3 and 8), scalable PHATE was used and subsampling was not performed except as indicated in the figures.”
Wishbone identifies bifurcating developmental trajectories from single-cell data (2016, methodology): “Randomly sampled subsets have previously been used to achieve more computationally efficient dimensionality reduction [32].”
Visualizing data as objects by DC (difference of convex) optimization (2017, methodology): “Leaving aside the statistical values ω, the purpose of representing dissimilarities between individuals reminds to MDS, [5, 16, 18, 29, 34, 35, 39, 52], which aims to represent the dissimilarity between individuals as empirical distances between points in an unbounded space of lower dimension.”
Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction (2006, background): “As stated in the paper, the use of landmark points has been recently proposed [43, 44] to significantly improve the efficiency of the Isomap algorithm when dealing with extremely large data sets.”
Toward a Quantitative Survey of Dimension Reduction Techniques (2019, methodology): “Techniques in this group are Metric Multidimensional Scaling ( MDS ) [17], Non-metric Multidimensional Scaling ( N-MDS ) [77] and Land-mark MDS ( L-MDS ) [78].”