title: A novel method to determine the number of latent dimensions with SVD
abstract: Determining the number of latent dimensions is a ubiquitous problem in machine
  learning. In this study, we introduce a novel method that relies on SVD to discover
  the number of latent dimensions. The general principle behind the method is to
  compare the curve of singular values of the SVD decomposition of a data set with
  the randomized data set curve. The inferred number of latent dimensions corresponds
  to the crossing point of the two curves. To evaluate our methodology, we
  compare it with competing methods such as Kaisers eigenvalue-greater-than-one
  rule (K1), Parallel Analysis (PA), Velicers MAP test (Minimum Average Partial).
  We also compare our method with the Silhouette Width (SW) technique which is
  used in different clustering methods to determine the optimal number of clusters.
  The result on synthetic data shows that the Parallel Analysis and our method have
  similar results and more accurate than the other methods, and that our methods is
  slightly better result than the Parallel Analysis method for the sparse data sets.
authors: Asana Neishabouri Michel Desmarais
hIndex: Michel Desmarais 24