| ... | ... | @@ -97,7 +97,7 @@ which is, nothing but the l2 norm we discussed in Chapter 2. This definition can |
|
|
|
Another popular option is the city block distance:
|
|
|
|
|
|
|
|
```math
|
|
|
|
Distance(x_{i},x_{i'}) = \sum_{m=1}^{M} \abs{x_{im}-x_{i'm}}
|
|
|
|
Distance(x_{i},x_{i'}) = \sum_{m=1}^{M} |x_{im}-x_{i'm}|
|
|
|
|
```
|
|
|
|
In this case, we will be considering the distance between features, not their squires. This is what we actually did with l1 norm in the regression analysis. We can visualize it as if we are in a car and trying to get from point A to B, in a city. This definition will tell us how many rows and columns of city blocks (buildings) we have to move horizontally and vertically for that journey.
|
|
|
|
|
| ... | ... | |
