Dmitry Storcheus
Author Archive

Dmitry Storcheus

Dmitry Storcheus served as a primary chair of The 1st International Workshop “Feature Extraction: Modern Questions and Challenges” at NIPS-2015. Previously, he has been a reviewer for multiple conferences: NIPS, AAA-I, FMA Annual Meeting and an Editor of the Journal of Machine Learning Research (JMLR), W&CP vol. 44. Dmitry Storcheus’ original research contribution in Dimensionality Reduction has received recognition from international experts and research institutions as well as produced a sequence of papers published in reviewed journals and conferences, which include “Theoretical Foundations of Learning Kernels for Supervised Kernel PCA” at NIPS, and “Generalization Bounds for Supervised Dimensionality Reduction” in JMLR.

Understanding Dimensionality Reduction And Its Applications
Data Science

Dimensionality reduction as means of feature extraction Feature extraction is a very broad and essential area of data science. It’s goal is to take out salient and informative features from input data, so that they can be used further in predictive algorithms. Modern data scientists observe large amounts of data,

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