(a) Describe the curse of dimensionality. Why does it make learning hard in high dimensional space...

 

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(a) Describe the curse of dimensionality. Why does it make learning hard in high dimensional space (b) Summarize main idea(s)in PCA and what is it used for? Is PCA a supervised or an unsupervised method? Why? (c) Assume that a three-dimensional (3D) feature space has the following covariance matrix. 0.681 -0.039 1.265 = 1 -0.039 0.187 -0.320 1.265 -0.32 3.092) Apply PCA to the 3D feature space vector x=[0.5,0.42,1.3] in order to project it onto the two-dimensional (2D) space. Show your work in detail. (a) Describe the curse of dimensionality. Why does it make learning hard in high dimensional space (b) Summarize main idea(s)in PCA and what is it used for? Is PCA a supervised or an unsupervised method? Why? (c) Assume that a three-dimensional (3D) feature space has the following covariance matrix. 0.681 -0.039 1.265 = 1 -0.039 0.187 -0.320 1.265 -0.32 3.092) Apply PCA to the 3D feature space vector x=[0.5,0.42,1.3] in order to project it onto the two-dimensional (2D) space. Show your work in detail.

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a The curse of dimensionality refers to the phenomena that arise when working in highdimensional spaces As the number of dimensions increases the volu... View the full answer