吴恩达机器学习笔记之非监督学习

Unsupervised learning

1. 监督学习的样本数据是有标签的，而非监督学习的数据样本是没有标签的。事先对这些数据的特征的特点不知道，需要用算法来帮我们进行分析，包括聚类分析，让算法自己去寻找数据中的结构，
2. 聚类——只是非监督学习的一种。
3. 鸡尾酒宴问题——非监督学习的另一种，让算法从一段嘈杂的音频中分离出不同的声音，实现这个功能只需要一行代码，涉及到奇异值分解。

   [W,s,v] = svd((repmat(sum(x.*x,1),size(x,1),1).*x)*x');

4. Octave编程环境，许多学习算法几行代码就可以实现。（目前Python是主流，但Octave值得了解一下。）

Unsupervised learning allows us to approach problems with little or no idea what our results
should look like. We can derive structure from data where we don't necessarily know the
effect of the variables.
We can derive this structure by clustering the data based on relationships among the variables
in the data.
With unsupervised learning there is no feedback based on the prediction results.
Example:
Clustering: Take a collection of 1,000,000 different genes, and find a way to automatically
group these genes into groups that are somehow similar or related by different variables,
such as lifespan, location, roles, and so on.
Non-clustering: The "Cocktail Party Algorithm", allows you to find structure in a chaotic（混乱的）
environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail
party).