TY  - CPAPER
AU  - Zhou, Qinghua
AU  - Sutton, Oliver J.
AU  - Zhang, Yu-Dong
AU  - Gorban, Alexander N.
AU  - Makarov Slizneva, Valeriy
AU  - Tyukin, Ivan Y.
PY  - 2023
DO  - 10.1109/ijcnn54540.2023.10191304
SN  - 2161-4407
UR  - https://hdl.handle.net/20.500.14352/99986
AB  - For learning algorithms, accessing large volumes of annotated data is highly desirable but not always available, especially in real-world scenarios. Accordingly, learning in the highdimensional and low-sample size (HDLS) domain is recognised as one of...
LA  - eng
KW  - Machine learning
KW  - High-dimensional low-samplesize (HDLS)
KW  - Quasi-orthogonality
TI  - Neuromorphic tuning of feature spaces to overcome the challenge of low-sample high-dimensional data
TY  - conference paper
ER  -