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Compile It!}, url={http://kripken.github.io/mloc_emscripten_talk}, publisher={Mozilla}, author={Zakai, Alon}} @misc{gpujs, title={gpu.js}, url={https://github.com/gpujs/gpu.js}, journal={GitHub}, year={2016}, month={Jan}} @misc{WebGL2, title={WebGL 2.0 Arrives}, url={https://www.khronos.org/blog/webgl-2.0-arrives}, journal={Khronos Group}, year={2017}, month={Feb}} @misc{WebAssembly, title={WebAssembly}, url={http://webassembly.org/}, journal={WebAssembly}} @ARTICLE{LSH, author = {{Andoni}, A. and {Razenshteyn}, I.}, title = "{Optimal Data-Dependent Hashing for Approximate Near Neighbors}", journal = {ArXiv e-prints}, archivePrefix = "arXiv", eprint = {1501.01062}, primaryClass = "cs.DS", keywords = {Computer Science - Data Structures and Algorithms}, year = 2015, month = jan, adsurl = {http://adsabs.harvard.edu/abs/2015arXiv150101062A}, } @article {SpringTemp, author = {Fruchterman, Thomas M. 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F.}, year={1997}, pages={207–-212}, } @article{kPCA, author = { Bernhard Schölkopf and Alexander Smola and Klaus-Robert Müller }, title = {Nonlinear Component Analysis as a Kernel Eigenvalue Problem}, journal = {Neural Computation}, volume = {10}, number = {5}, pages = {1299-1319}, year = {1998}, doi = {10.1162/089976698300017467}, URL = { https://doi.org/10.1162/089976698300017467 }, eprint = { https://doi.org/10.1162/089976698300017467 } , abstract = { A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map—for instance, the space of all possible five-pixel products in 16 × 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition. } } @ARTICLE{Sammon, author={J. W. Sammon}, journal={IEEE Transactions on Computers}, title={A Nonlinear Mapping for Data Structure Analysis}, year={1969}, volume={C-18}, number={5}, pages={401-409}, keywords={Clustering, dimensionality reduction, mappings, multidimensional scaling, multivariate data analysis, nonparametric, pattern recognition, statistics.;Algorithm design and analysis;Computer errors;Data analysis;Data structures;Euclidean distance;Helium;Multidimensional systems;Pattern recognition;Testing;Vectors;Clustering, dimensionality reduction, mappings, multidimensional scaling, multivariate data analysis, nonparametric, pattern recognition, statistics.}, doi={10.1109/T-C.1969.222678}, ISSN={0018-9340}, month={May},} @article{LMDS, title = "Local multidimensional scaling", journal = "Neural Networks", volume = "19", number = "6", pages = "889 - 899", year = "2006", note = "Advances in Self Organising Maps - WSOM’05", issn = "0893-6080", doi = "https://doi.org/10.1016/j.neunet.2006.05.014", url = "http://www.sciencedirect.com/science/article/pii/S0893608006000724", author = "Jarkko Venna and Samuel Kaski", keywords = "Information visualization, Manifold extraction, Multi-dimensional scaling (MDS), Nonlinear dimensionality reduction, Non-linear projection, Gene expression" } @misc{eslint, title={ESLint - Pluggable JavaScript linter}, url={https://eslint.org/}, journal={ESLint - Pluggable JavaScript linter}} @misc{js-beautify, title={js-beautify}, url={https://www.npmjs.com/package/js-beautify}, journal={npm}}