👉 Factor computing is a method used to efficiently represent and process large matrices by decomposing them into smaller, more manageable factors. This technique reduces computational complexity and memory usage, making it particularly useful for tasks like machine learning and data analysis involving high-dimensional data. By breaking down a matrix into factors such as U, Σ, and V^T (where U and V are orthogonal matrices, and Σ is a diagonal matrix of singular values), factor computing allows for faster operations like matrix multiplication and inversion, while preserving essential information. This approach is crucial in handling big data efficiently, as it enables the use of algorithms that would otherwise be computationally infeasible due to the sheer size of the matrices involved.
