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

LDA is a supervised learning technique.

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

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

PCA is affected by the order of the input features.

PCA can be applied directly to categorical data without preprocessing.

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

Standardization is optional when performing PCA.

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

The principal components are orthogonal to each other.