Mixing
norming vector for more than three dimensional spaces
$begingroup$
This has to do with Vector space model (or term vector model) shown here.
I'm preparing a presentation on document similarity, which includes the topic of norming vectors. Since the measure of similarity is the angle, which is a 2D structure by definition, what are some reasons to want to calculate the norm for more than 2 or 3 dimensional spaces ?
linear-algebra linear-transformations vectorization
asked Jan 6 at 6:49
Uncle VUncle V
61
$endgroup$
add a comment |
$begingroup$
This has to do with Vector space model (or term vector model) shown here.
I'm preparing a presentation on document similarity, which includes the topic of norming vectors. Since the measure of similarity is the angle, which is a 2D structure by definition, what are some reasons to want to calculate the norm for more than 2 or 3 dimensional spaces ?
linear-algebra linear-transformations vectorization
asked Jan 6 at 6:49
Uncle VUncle V
61
$endgroup$
$begingroup$
Two linearly independent vectors define a plane regardless of the dimension of the ambient space.
$endgroup$
– amd
Jan 6 at 8:11
$begingroup$
Thank you, amd. I can't disagree with that. What you see in the article though is two multidimensional vectors (d and q) turning into the two dimensional vectors after the unspecified number of norming steps. I'm trying to build sound arguments in favor of doing that.
$endgroup$
– Uncle V
Jan 6 at 8:44
add a comment |
$begingroup$
This has to do with Vector space model (or term vector model) shown here.
I'm preparing a presentation on document similarity, which includes the topic of norming vectors. Since the measure of similarity is the angle, which is a 2D structure by definition, what are some reasons to want to calculate the norm for more than 2 or 3 dimensional spaces ?
linear-algebra linear-transformations vectorization
asked Jan 6 at 6:49
Uncle VUncle V
61
$endgroup$
This has to do with Vector space model (or term vector model) shown here.
I'm preparing a presentation on document similarity, which includes the topic of norming vectors. Since the measure of similarity is the angle, which is a 2D structure by definition, what are some reasons to want to calculate the norm for more than 2 or 3 dimensional spaces ?
linear-algebra linear-transformations vectorization
linear-algebra linear-transformations vectorization
asked Jan 6 at 6:49
Uncle VUncle V
61
asked Jan 6 at 6:49
Uncle VUncle V
61
edited Jan 6 at 8:04
Uncle V
asked Jan 6 at 6:49
Uncle VUncle V
61
asked Jan 6 at 6:49
Uncle VUncle V
61
asked Jan 6 at 6:49
Uncle VUncle V
61
61
$begingroup$
Two linearly independent vectors define a plane regardless of the dimension of the ambient space.
$endgroup$
– amd
Jan 6 at 8:11
$begingroup$
Thank you, amd. I can't disagree with that. What you see in the article though is two multidimensional vectors (d and q) turning into the two dimensional vectors after the unspecified number of norming steps. I'm trying to build sound arguments in favor of doing that.
$endgroup$
– Uncle V
Jan 6 at 8:44
add a comment |
$begingroup$
Two linearly independent vectors define a plane regardless of the dimension of the ambient space.
$endgroup$
– amd
Jan 6 at 8:11
$begingroup$
Thank you, amd. I can't disagree with that. What you see in the article though is two multidimensional vectors (d and q) turning into the two dimensional vectors after the unspecified number of norming steps. I'm trying to build sound arguments in favor of doing that.
$endgroup$
– Uncle V
Jan 6 at 8:44
$begingroup$
Two linearly independent vectors define a plane regardless of the dimension of the ambient space.
$endgroup$
– amd
Jan 6 at 8:11
$begingroup$
Two linearly independent vectors define a plane regardless of the dimension of the ambient space.
$endgroup$
– amd
Jan 6 at 8:11
$begingroup$
Thank you, amd. I can't disagree with that. What you see in the article though is two multidimensional vectors (d and q) turning into the two dimensional vectors after the unspecified number of norming steps. I'm trying to build sound arguments in favor of doing that.
$endgroup$
– Uncle V
Jan 6 at 8:44
$begingroup$
Thank you, amd. I can't disagree with that. What you see in the article though is two multidimensional vectors (d and q) turning into the two dimensional vectors after the unspecified number of norming steps. I'm trying to build sound arguments in favor of doing that.
$endgroup$
– Uncle V
Jan 6 at 8:44
add a comment |
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$begingroup$
Two linearly independent vectors define a plane regardless of the dimension of the ambient space.
$endgroup$
– amd
Jan 6 at 8:11
$begingroup$
Thank you, amd. I can't disagree with that. What you see in the article though is two multidimensional vectors (d and q) turning into the two dimensional vectors after the unspecified number of norming steps. I'm trying to build sound arguments in favor of doing that.
$endgroup$
– Uncle V
Jan 6 at 8:44