The L1 distance, also known as Manhattan distance, has several advantages over the L2 distance, also known as Euclidean distance.

1. Better handling of high dimensional data: In high dimensional spaces, Euclidean distance can be greatly affected by the curse of dimensionality, where the distance between most pairs of points tends to a common value, making it less useful. L1 distance is not affected by this problem to the same extent.

2. Useful for coordinate based measurements: L1 distance is calculated as the sum of the absolute differences of their coordinates. Therefore, it is more appropriate when you want to measure distances in a grid-like path, like in a chessboard or city block grid.

3. Better for data where location of occurrence is less important: L1 distance is less sensitive to outliers than L2 distance. This means that if the location of occurrence is less important in your data, L1 might be a better choice.

4. Shows the difference between sets of values: L1 distance can be used to measure how different two sets of values are. This can be useful in many applications, such as comparing two images or two audio signals.

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The L1 distance, also known as Manhattan distance, has several advantages over the L2 distance, also known as Euclidean distance.

1. Better handling of high dimensional data: In high dimensional spaces, Euclidean distance can be greatly affected by the curse of dimensionality, where the distance between most pairs of points tends to a common value, making it less useful. L1 distance is not affected by this problem to the same extent.

2. Useful for coordinate based measurements: L1 distance is calculated as the sum of the absolute differences of their coordinates. Therefore, it is more appropriate when you want to measure distances in a grid-like path, like in a chessboard or city block grid.

3. Better for data where location of occurrence is less important: L1 distance is less sensitive to outliers than L2 distance. This means that if the location of occurrence is less important in your data, L1 might be a better choice.

4. Shows the difference between sets of values: L1 distance can be used to measure how different two sets of values are. This can be useful in many applications, such as comparing two images or two audio signals.

Knowee AI · Accepted Answer

The L1 distance, also known as Manhattan distance, has several advantages over the L2 distance, also known as Euclidean distance.

1. Better handling of high dimensional data: In high dimensional spaces, Euclidean distance can be greatly affected by the curse of dimensionality, where the distance between most pairs of points tends to a common value, making it less useful. L1 distance is not affected by this problem to the same extent.

2. Useful for coordinate based measurements: L1 distance is calculated as the sum of the absolute differences of their coordinates. Therefore, it is more appropriate when you want to measure distances in a grid-like path, like in a chessboard or city block grid.

3. Better for data where location of occurrence is less important: L1 distance is less sensitive to outliers than L2 distance. This means that if the location of occurrence is less important in your data, L1 might be a better choice.

4. Shows the difference between sets of values: L1 distance can be used to measure how different two sets of values are. This can be useful in many applications, such as comparing two images or two audio signals.

What is an advantage of the L1 distance over L2?1 pointIt can better handle high dimensional data.It's useful for coordinate based measurements.It's better for data where location of occurrence is less important.It shows the difference between sets of values.

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