如何理解机器学习/统计学中的各种范数norm | L1 | L2 | 使用哪种regularization方法？

参考：

L1 Norm Regularization and Sparsity Explained for Dummies 专为小白解释的文章，文笔十分之幽默

1. why does a small L1 norm give a sparse solution?
2. why does a sparse solution avoid over-fitting?
3. what does regularization do really?

减少feature的数量可以防止over fitting，尤其是在特征比样本数多得多的情况下。

L1就二维而言是一个四边形（L1 norm is |x| + |y|），它是只有形状没有大小的，所以可以不断伸缩。我们得到的参数是一个直线（两个参数时），也就是我们有无数种取参数的方法，但是我们想满足L1的约束条件，所以 要选择相交点的参数组。

Then why not letting p < 1? That’s because when p < 1, there are calculation difficulties. 所以我们通常只在L1和L2之间选，这是因为计算问题，并不是不能。

l0-Norm, l1-Norm, l2-Norm, … , l-infinity Norm

  where 

就是一个简单的公式而已，所有的范数瞬间都可以理解了。（注意范数的写法，写在下面，带双竖杠）

Before answering your question I need to edit that Manhattan norm is actually L1 norm and Euclidean norm is L2.

As for real-life meaning, Euclidean norm measures the beeline/bird-line distance, i.e. just the length of the line segment connecting two points. However, when we move around, especially in a crowded city area like Manhattan, we obviously cannot follow a straight line (unless you can fly like a bird). Instead, we need to follow a grid-like route, e.g. 3 blocks to teh west, then 4 blocks to the south. The length of this grid route is the Manhattan norm.

之前的印象是L1就是Lasso，是一个四边形，相当于绝对值。

L2就是Ridge，相当于是一个圆。