Feature extraction is a dimensionality reduction technique. Unlike feature selection, which selects and retains the most significant attributes, feature extraction actually transforms the attributes. The transformed attributes, or features, are linear combinations of the original attributes.

The feature extraction technique results in a much smaller and richer set of attributes. The maximum number of features can be user-specified or determined by the algorithm. By default, the algorithm determines it.

Models built on extracted features can be of higher quality, because fewer and more meaningful attributes describe the data.

Feature extraction projects a data set with higher dimensionality onto a smaller number of dimensions. As such it is useful for data visualization, since a complex data set can be effectively visualized when it is reduced to two or three dimensions.

Some applications of feature extraction are latent semantic analysis, data compression, data decomposition and projection, and pattern recognition. Feature extraction can also be used to enhance the speed and effectiveness of machine learning algorithms.

Feature extraction can be used to extract the themes of a document collection, where documents are represented by a set of key words and their frequencies. Each theme (feature) is represented by a combination of keywords. The documents in the collection can then be expressed in terms of the discovered themes.

Understand the algorithms used for feature extraction.

OML4SQL supports these feature extraction algorithms:

• Explicit Semantic Analysis (ESA).

• Non-Negative Matrix Factorization (NMF).

• Singular Value Decomposition (SVD) and Prediction Component Analysis (PCA).