Take a look at a real-world example of understanding direction of maximum variance in the following picture representing Taj Mahal of Agra. For example, in a movie, it is okay to identify objects by 2-dimensions as these dimensions represent direction of maximum variance. It is the direction of maximum variance of data that helps us identify an object. This is represented using PCA1 (first maximum variance) and PC2 (2nd maximum variance). In the diagram given below, note the directions of maximum variance of data. ![]() It aims to find the directions of maximum variance in high-dimensional data and projects the data onto a new subspace with equal or fewer dimensions than the original one. Principal component analysis (PCA) is an unsupervised linear transformation technique which is primarily used for feature extraction and dimensionality reduction. ![]() How is PCA different than other feature selection techniques?.
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