STA 561 : Probabilistic machine learning Factor Analysis ( 10 / 2 / 13 ) Lecturer :
نویسندگان
چکیده
Commonly, factor analysis is a method for dimension reduction: it reduces the dimension of the original space by representing the full matrix as the product of two much smaller matrices plus random noise. We are able to model the variability among the observed variables by projection from a high dimensional space to a low dimensional one, such from 3D to 2D as shown in Figure 1(a). To do this, let’s start with a matrix of real-valued latent variables: X ∈ <n×p. The observed variables are modeled as a linear combination of the latent variables plus Gaussian error as follows:
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