Non Intrusive Load Monitoring has been introduced 30 years ago in order to monitor the electric consumption of specific equipments inside a building without the need of installing multiples sensors. During three decades, researchers and industrials have described the NILM problems according to the electric data available, the desired quantity to be monitored and the application it was used for. As a consequence of the multitude of choices, a lot of different formulations can be found in the literature. This diversity makes it difficult for researchers from general domains such as machine learning to tackle the NILM problem. In this paper we aim at defining the NILM problem as a Matrix Factorization task using high frequency measurements and also to review methods to solve this problem. We start by defining the general concepts driving the NILM problem and then show how to cast high frequency NILM into a Matrix Factorization problem. Once casted as a machine learning problem, we will review general purposes algorithms applicable to this problem such as Independent Component Analysis, Sparse Coding or Semi Non-negative Matrix Factorization and specific NILM methods such as BOLT and IVMF.