Normalização L1, também conhecida como Regularização L1 or Lasso normalization, is a method used in various fields of aprendizado de máquina and dados útil to scale data and melhorar o desempenho do modelo. The primary goal of L1 Normalization is to adjust the values of the data points such that their total absolute value equals one. This is achieved by dividing each individual value by the sum of the absolute values of all data points in the dataset.
A fórmula para a Normalização L1 pode ser expressa como:
Aqui, x represents an individual data point, and the denominator is the sum of the absolute values of all data points in the dataset. This process ensures that the transformed data will be bounded within the range of -1 to 1, leading to a more uniform distribution of values.
L1 Normalization is particularly useful in scenarios where the data may have varying scales or units, as it helps to eliminate biases that might arise from such differences. It is commonly used in algorithms such as Lasso regression, where it encourages sparsity in the model by shrinking some coefficients to zero. This characteristic makes L1 Normalization a valuable technique for seleção de variáveis em conjuntos de dados de alta dimensionalidade.
No geral, a Normalização L1 desempenha um papel crucial na preparação de Visionamento Monocular, ensuring that each feature contributes equally to the final outcome.