Unsupervised learning無監督學習

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 environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).

 

無監督學習容許咱們在不知道結果的狀況下去解決問題。咱們能夠從那些變量對結果影響不大的數據中導出結構

咱們能夠經過數據之間的變量關係來對數據進行聚類，從而推導出這種結構

無監督學習對於預測結果沒有反饋

ex：

聚類：收集1000000中不一樣的基因集合，而後找到一種方法將這些基因自動分組成類似或者相關的不一樣變量組，如壽命、位置、角色等

非聚類：雞尾酒宴會算法，容許在混亂的環境中查找結構（從嘈雜的雞尾酒宴會上分辨出講話的聲音和音樂的聲音）