Visualization of High Dimensional data

Reference:

  • http://www.codeproject.com/Tips/788739/Visualization-of-High-Dimensional-Data-using-t-SNE
  • https://github.com/lvdmaaten/bhtsne/
  • http://nbviewer.jupyter.org/urls/gist.githubusercontent.com/AlexanderFabisch/1a0c648de22eff4a2a3e/raw/59d5bc5ed8f8bfd9ff1f7faa749d1b095aa97d5a/t-SNE.ipynb
  • http://www.nature.com/ni/journal/v15/n4/fig_tab/ni.2842_SF2.html
  • http://www.nature.com/nbt/journal/v33/n5/full/nbt.3192.html?cookies=accepted#supplementary-information
  • http://www.nature.com/ni/journal/v17/n4/full/ni.3368.html
  • http://stats.stackexchange.com/questions/69157/why-do-we-need-to-normalize-data-before-analysis
  • http://stats.stackexchange.com/questions/105592/not-normalizing-data-before-pca-gives-better-explained-variance-ratio
  • http://stackoverflow.com/questions/14432557/matplotlib-scatter-plot-with-different-text-at-each-data-point
  • http://nbviewer.jupyter.org/urls/gist.githubusercontent.com/AlexanderFabisch/1a0c648de22eff4a2a3e/raw/59d5bc5ed8f8bfd9ff1f7faa749d1b095aa97d5a/t-SNE.ipynb

  • http://scikit-learn.org/stable/modules/clustering.html#clustering-performance-evaluation
  • http://stats.stackexchange.com/questions/72839/how-to-use-r-prcomp-results-for-prediction

  • http://www.sthda.com/english/wiki/principal-component-analysis-in-r-prcomp-vs-princomp-r-software-and-data-mining
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积微,月不胜日,时不胜月,岁不胜时。凡人好敖慢小事,大事至,然后兴之务之。如是,则常不胜夫敦比于小事者矣!何也?小事之至也数,其悬日也博,其为积也大。大事之至也希,其悬日也浅,其为积也小。故善日者王,善时者霸,补漏者危,大荒者亡!故,王者敬日,霸者敬时,仅存之国危而后戚之。亡国至亡而后知亡,至死而后知死,亡国之祸败,不可胜悔也。霸者之善著也,可以时托也。王者之功名,不可胜日志也。财物货宝以大为重,政教功名者反是,能积微者速成。诗曰:德如毛,民鲜能克举之。此之谓也。

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