Natural Law. Senseable_city_lab01. Infographics & Data Visualization. A visual exploration on mapping complex networks. Principal Manifolds for Data Visualization and Dimension Reduction. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described Presentation of algorithms is supplemented by case studies In 1901 Karl Pearson invented Principal Component Analysis (PCA).
Since then, PCA serves as a prototype for many other tools of data analysis, visualization and dimension reduction: Independent Component Analysis (ICA), Multidimensional Scaling (MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SOM), etc. The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described as well. Data Visualizations, Challenges, Community. Sourcemap: where things come from. 50 Well-Designed Infographics About Design (Part 2) Continuing from last week’s Part 1, here are another roughly 6,000 words of design facts, figures, and information contained, Tardis-like, in an article of only 600 words.
In other words, this article is bigger on the inside than it is on the outside. Alas, it’s still too small to contain all the well-designed infographics I found about graphic design, Web design, photography, freelancing, and, of course, infographic design. I had to cut so many great infographics just to keep this two-parter a reasonable length.
Urban Population Growth 1990-2015.