Our new paper Quantile–Quantile Embedding for Distribution Transformation and Manifold Embedding (Ghojogh et al., 2021) on a novel method for shaping distributions using a new twist on classic quantile-quantile plotting methods has been accepted for publication in the Elsevier journal Machine Learning with Applications (MLWA).
This paper comes out of the Manifold Learning research topic in out lab led by postdoc Benyamin Ghojogh and was one of the components of his recent completed PhD thesis (missing reference) here in the lab.
References:
Quantile–Quantile Embedding for distribution transformation and manifold embedding with ability to choose the embedding distribution
Machine Learning with Applications (MLWA).
6,
2021.
We propose a new embedding method, named Quantile-Quantile Embedding (QQE), for distribution transformation and manifold embedding with the ability to choose the embedding distribution. QQE, which uses the concept of quantile-quantile plot from visual statistical tests, can transform the distribution of data to any theoretical desired distribution or empirical reference sample. Moreover, QQE gives the user a choice of embedding distribution in embedding the manifold of data into the low dimensional embedding space. It can also be used for modifying the embedding distribution of other dimensionality reduction methods, such as PCA, t-SNE, and deep metric learning, for better representation or visualization of data. We propose QQE in both unsupervised and supervised forms. QQE can also transform a distribution to either an exact reference distribution or its shape. We show that QQE allows for better discrimination of classes in some cases. Our experiments on different synthetic and image datasets show the effectiveness of the proposed embedding method.