**5. Variants of PCA**

To overcome the limitations of PCA, there are different types of PCA are available that suit for the appropriate type of data are listed below [7, 8]:


### **5.1 Normal PCA**

PCA in Machine learning is applied for unsupervised learning to reduce the dimension of the data from high dimensional space to low dimensional space. The above section discusses the standard normal PCA, which applies to most of the datasets as a default form of PCA using unsupervised learning. To construct any type of PCA especially for normal PCA the above discussed five significant steps are involved for dimensionality reduction [9, 10]. The following sessions briefly discuss the other variants of PCA in machine learning and its characteristics.
