466 citations · 780 across the 6 of their papers we have counts for
6 papers
A surrogate loss function for optimization of score in binary classification with imbalanced data
Namgil Lee, Heejung Yang, Hojin Yoo
The score is a commonly used measure of classification performance, which plays crucial roles in classification tasks with imbalanced data sets. However, the score cann…
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives
A. Cichocki, A-H. Phan, Q. Zhao +4
Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-ord…
Low-Rank Tensor Networks for Dimensionality Reduction and Large-Scale Optimization Problems: Perspectives and Challenges PART 1
A. Cichocki, N. Lee, I. V. Oseledets +3
Machine learning and data mining algorithms are becoming increasingly important in analyzing large volume, multi-relational and multi--modal datasets, which are often conveniently…
Regularized Computation of Approximate Pseudoinverse of Large Matrices Using Low-Rank Tensor Train Decompositions
Namgil Lee, Andrzej Cichocki
We propose a new method for low-rank approximation of Moore-Penrose pseudoinverses (MPPs) of large-scale matrices using tensor networks. The computed pseudoinverses can be useful f…
Very Large-Scale Singular Value Decomposition Using Tensor Train Networks
Namgil Lee, Andrzej Cichocki
We propose new algorithms for singular value decomposition (SVD) of very large-scale matrices based on a low-rank tensor approximation technique called the tensor train (TT) format…
Fundamental Tensor Operations for Large-Scale Data Analysis in Tensor Train Formats
Namgil Lee, Andrzej Cichocki
We discuss extended definitions of linear and multilinear operations such as Kronecker, Hadamard, and contracted products, and establish links between them for tensor calculus. The…