Similarity using euclidean distance, cosine similarity given co-occurrence matrix
In class, our professor gave us a co-occurrence matrix
I thought that the euclidean distance for dog, cat would be:
$sqrt{(115/156 - 52/82.5)^2 + (89/156 - 58/82.5)^2 + (10/256 - 4/82.5)^2 + (42/156 - 4/82.5)^2+ (33/156 - 6/82.5)^2 + (17/156- 26/82.5)^2}$
Then you do $frac{1}{1 + d(dog, cat)} = frac{1}{1 + 0.37} = 0.7280128371$ However, this is not the same as his answer.
Is there a different way of doing euclidean distance?
matrices vector-spaces vectors euclidean-geometry
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In class, our professor gave us a co-occurrence matrix
I thought that the euclidean distance for dog, cat would be:
$sqrt{(115/156 - 52/82.5)^2 + (89/156 - 58/82.5)^2 + (10/256 - 4/82.5)^2 + (42/156 - 4/82.5)^2+ (33/156 - 6/82.5)^2 + (17/156- 26/82.5)^2}$
Then you do $frac{1}{1 + d(dog, cat)} = frac{1}{1 + 0.37} = 0.7280128371$ However, this is not the same as his answer.
Is there a different way of doing euclidean distance?
matrices vector-spaces vectors euclidean-geometry
add a comment |
In class, our professor gave us a co-occurrence matrix
I thought that the euclidean distance for dog, cat would be:
$sqrt{(115/156 - 52/82.5)^2 + (89/156 - 58/82.5)^2 + (10/256 - 4/82.5)^2 + (42/156 - 4/82.5)^2+ (33/156 - 6/82.5)^2 + (17/156- 26/82.5)^2}$
Then you do $frac{1}{1 + d(dog, cat)} = frac{1}{1 + 0.37} = 0.7280128371$ However, this is not the same as his answer.
Is there a different way of doing euclidean distance?
matrices vector-spaces vectors euclidean-geometry
In class, our professor gave us a co-occurrence matrix
I thought that the euclidean distance for dog, cat would be:
$sqrt{(115/156 - 52/82.5)^2 + (89/156 - 58/82.5)^2 + (10/256 - 4/82.5)^2 + (42/156 - 4/82.5)^2+ (33/156 - 6/82.5)^2 + (17/156- 26/82.5)^2}$
Then you do $frac{1}{1 + d(dog, cat)} = frac{1}{1 + 0.37} = 0.7280128371$ However, this is not the same as his answer.
Is there a different way of doing euclidean distance?
matrices vector-spaces vectors euclidean-geometry
matrices vector-spaces vectors euclidean-geometry
asked Dec 5 '18 at 3:52
MattMatt
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