FIGURE 3. Main Approaches for Feature Engineering and Learning.

The manually engineered approaches are manually designed to extract certain types of features from the data. For example, local binary pattern and scale-invariant feature transform derive the properties from the image such as object recognition or edge detection. The classic learning techniques use data samples to learn their characteristics for dimensionality reduction, but they have limitations in their data modeling techniques, such as linearity, sparsity, and lack of hierarchical representation. Principal component analysis (PCA) applies orthogonal transformation to produce linear combination of uncorrelated variables that best explains the variability of the data, whereas independent component analysis transforms the dataset into independent components to reduce dimensionality. Deep learning methods, however, can learn complex features from the data at multiple levels in various hidden layers.