STAT 154 Lecture Notes - Lecture 24: Eigendecomposition Of A Matrix, Linear Combination, Cluster Analysis
17 Nov 2018
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Now we can change the problem to find c so that. Xi cy loft t cf fill di hi how to construct the vector so that. Ruses standard from r is data , not. Now we reduces can its change the max problem of. If will change completely not 10070 is in we x*p= xp. X and net your final grade , things. De nition: a methodology to suppress a set of high dimensional data into lower dimension data with valid information. Each dimension in the transformed data is a linear combination of the original x-variables, which has maximum variance among all linear combinations. It counts for as much variation in the data as possible. Principal component analysis: maximizing the between group variance/information contained in the data. Looking for a low-dimensional representation of the observations that explain a good fraction of the variance: cluster analysis: minimizing the within group (cluster) variation.
