Category: linear algebra
Mean as a Projection
This tutorial explains how mean can be viewed as an orthogonal projection onto a subspace defined by the span of an all 1’s vector (i.e., basis vector). Suppose that \( \vec{y} \in \mathbb{R}^n \) and \( L \subset \mathbb{R}^n\) is...
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Orthogonal Projection
This tutorial explains what an orthogonal projection is in linear algebra. Further, it provides proof that the difference between a vector and a subspace is orthogonal to that subspace. Let’s define two vectors, \(\vec{X} \) and \(\vec{Y} \), and we...
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Difference between Space and Subspace
This tutorial explains the difference between space and subspace. What is space? The most simple example of space is the two-dimensional space, \( \mathbb{R}^2 \). You can visualize it as the xy-coordinate plane. \( \mathbb{R}^2 \) is a vector space...
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Linear Subspace
A linear subspace or vector subspace is a vector space that is a subset of some larger vector space. To be considered a linear subspace, a vector set needs to meet the following 3 requirements. For instance, if we say...
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Vector and Arrow in Space
This tutorial provides examples to explain vectors and arrows in space (vector visualization). It includes 2 and 3-dimension vectors as well as vector addition and subtraction. Example 1: 2 dimension vectors \( \vec{V_1} =(3, 2 ) = \left[\begin{array}{ccc}3\\2 \end{array}\right]\) Note...
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Orthonormal Vectors: Definitions and Examples
Two Orthogonal Vectors Definition: Two vectors are orthogonal if they are perpendicular to each other. That is, the dot product of the two vectors is zero. The following is an example of two orthonormal vectors. \( \vec{V_1} =\left[\begin{array}{ccc}1\\0\\-1\end{array}\right]\), \( \vec{V_2}...
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