Analysis pca general massive data visualiza8on tips

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Unformatted text preview: Readings Assignment 5 & Mid- Term Presenta8on 3 2/22/12 Principle Components Analysis (PCA) •  Takes high dimensional data, where some/many axes are correlated h\p://cnx.org/content/m11461/latest/ •  Reduce to a smaller set of dimensions that are not correlated •  Dimensions/axes form a new basis/coordinate system PCA Example: Material Reflectance Model How many eigenvalues/ dimensions are necessary to represent this data? –  Each example from the original data can be defined as a linear combina8on of the new axes •  Essen8ally we want to find the internal structure that best explains the variance in the data PCA Example: Face Modeling & Transfer Matusik, Pfister, Brand, & McMillan, “A Data- Driven Reflectance Model” SIGGRAPH 2002 Today’s Class •  •  •  •  •  •  •  •  Readings for this Week Examples of High Dimensional Data Parallel Coordinates Data Clustering Principle Components Analysis (PCA) General Massive Data Visualiza8on Tips Next Week’s Reading...
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This document was uploaded on 03/16/2014 for the course CSCI 4973 at Rensselaer Polytechnic Institute.

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