(f) Given a vector of real numbers, if a raw number in the vector is smaller than the mean, its corresponding normalized value will be negative after z-score standardization. (g) Given any two vectors of the same length, the L2 distance between them is always smaller than or equal to the L₁ distance between them. (h) The covariance matrix (of one random vector) has square shape. (i) Let Р and 9 be two different distributions. The KL divergence between p and q is the same as the KL divergence between q and p. (j) Principal Component Analysis (PCA) can generate new features.
(f) Given a vector of real numbers, if a raw number in the vector is smaller than the mean, its corresponding normalized value will be negative after z-score standardization. (g) Given any two vectors of the same length, the L2 distance between them is always smaller than or equal to the L₁ distance between them. (h) The covariance matrix (of one random vector) has square shape. (i) Let Р and 9 be two different distributions. The KL divergence between p and q is the same as the KL divergence between q and p. (j) Principal Component Analysis (PCA) can generate new features.
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Transcribed Image Text:(f)
Given a vector of real numbers, if a raw number in the vector is smaller than the
mean, its corresponding normalized value will be negative after z-score standardization.
(g)
Given any two vectors of the same length, the L2 distance between them is
always smaller than or equal to the L₁ distance between them.
(h) The covariance matrix (of one random vector) has square shape.
(i)
Let p and q be two different distributions. The KL divergence between p
and 9 is the same as the KL divergence between q and p.
(j)
Principal Component Analysis (PCA) can generate new features.
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