Description: Orthogonalization is the process of transforming a set of vectors in a vector space so that they become orthogonal to each other, meaning that the inner product between each pair of vectors is zero. This concept is fundamental in various areas of mathematics and computing, as it allows for the simplification of complex problems by breaking them down into independent components. In the context of quantum computing, orthogonalization is used to ensure that quantum states are distinguishable. In machine learning, orthogonalization can be crucial for feature selection, helping to reduce data dimensionality and improve model efficiency. Additionally, in deep learning and convolutional neural networks, it is employed to optimize training convergence and enhance model generalization. In summary, orthogonalization is a powerful technique used to improve the quality and efficiency of algorithms across multiple technological disciplines.
