The first principal component always explains the majority of the variance in the data.

Standardization is optional when performing PCA.

The first principal component explains the most variance in the data.

PCA can be used to visualize high-dimensional data in 2D or 3D.

PCA can be applied directly to categorical data without preprocessing.

PCA is affected by the order of the input features.

Which pattern recognition method relies on analyzing the structure and relationships of primitive subpatterns

PCA can be applied to datasets with missing values without any preprocessing.

LDA is a supervised learning technique.

The principal components are orthogonal to each other.