A regularization that penalizes a model’s total nonzero weights. A model with L1 nonzero weights is penalized more than one with L0. L0 regularization is rare.
Applications of L0 Regularization
Feature selection
In machine learning, L0 regularization is used in feature selection to identify a subset of the most relevant features from a larger set of features. L0 – regularization penalizes non-zero weights and can ultimately result in a sparse model with only the desired features.
Image & Signal Processing
L0 – regularization removes noise from images or signals to build a sparse output devoid of irrelevant details.
Text mining
By identifying the most suitable words or phrases, L0 – regularization can help in text classification and sentiment analysis.
Bioinformatics
In gene expression analysis, L0 – regularization is used for gene selection, aiming to identify a subcategory of genes that are more suitable to analyze a disease or a condition.
