Feature engineering

Feature engineering is a preprocessing step in supervised machine learning and statistical modeling[1] which transforms raw data into a more effective set of inputs.

By providing models with relevant information, feature engineering significantly enhances their predictive accuracy and decision-making capability.

The non-negativity constraints on coefficients of the feature vectors mined by the above-stated algorithms yields a part-based representation, and different factor matrices exhibit natural clustering properties.

Several extensions of the above-stated feature engineering methods have been reported in literature, including orthogonality-constrained factorization for hard clustering, and manifold learning to overcome inherent issues with these algorithms.

An example is Multi-view Classification based on Consensus Matrix Decomposition (MCMD),[2] which mines a common clustering scheme across multiple datasets.

MCMD is designed to output two types of class labels (scale-variant and scale-invariant clustering), and: Coupled matrix and tensor decompositions are popular in multi-view feature engineering.

Feature engineering can be a time-consuming and error-prone process, as it requires domain expertise and often involves trial and error.

[39] In addition, choosing the right architecture, hyperparameters, and optimization algorithm for a deep neural network can be a challenging and iterative process.