MLSP-27.4
DEEP DETERMINISTIC INDEPENDENT COMPONENT ANALYSIS FOR HYPERSPECTRAL UNMIXING
Hongming Li, Jose Principe, University of Florida, United States of America; Shujian Yu, The Arctic University of Norway, Norway
Session:
Subspace and Manifold Learning
Track:
Machine Learning for Signal Processing
Location:
Gather Area F
Presentation Time:
Tue, 10 May, 22:00 - 22:45 China Time (UTC +8)
Tue, 10 May, 14:00 - 14:45 UTC
Tue, 10 May, 14:00 - 14:45 UTC
Session Chair:
Anand Sarwate, Rutgers University
Session MLSP-27
MLSP-27.1: CONTROLLING THE FRÉCHET VARIANCE IMPROVES BATCH NORMALIZATION ON THE SYMMETRIC POSITIVE DEFINITE MANIFOLD
Reinmar Kobler, Jun-ichiro Hirayama, Motoaki Kawanabe, RIKEN, Japan
MLSP-27.2: SUBSPACE CLUSTERING USING UNSUPERVISED DATA AUGMENTATION
Maryam Abdolali, Nicolas Gillis, University of Mons, Belgium
MLSP-27.3: PRIVATE LEARNING VIA KNOWLEDGE TRANSFER WITH HIGH-DIMENSIONAL TARGETS
Dominik Fay, Tobias Oechtering, KTH Royal Institute of Technology, Sweden; Jens Sjölund, Uppsala University, Sweden
MLSP-27.4: DEEP DETERMINISTIC INDEPENDENT COMPONENT ANALYSIS FOR HYPERSPECTRAL UNMIXING
Hongming Li, Jose Principe, University of Florida, United States of America; Shujian Yu, The Arctic University of Norway, Norway
MLSP-27.5: LABEL-AWARE RANKED LOSS FOR ROBUST PEOPLE COUNTING USING AUTOMOTIVE IN-CABIN RADAR
Lorenzo Servadei, Huawei Sun, Julius Ott, Daniela Sanchéz Lopera, Infineon Technologies AG / Technical University of Munich, Germany; Michael Stephan, Thomas Stadelmayer, Infineon Technologies AG / Friedrich-Alexander-University of Erlangen Nuremberg, Germany; Souvik Hazra, Avik Santra, Infineon Technologies AG, Germany; Robert Wille, Johannes Kepler University Linz, Germany
MLSP-27.6: DEEPHULL: FAST CONVEX HULL APPROXIMATION IN HIGH DIMENSIONS
Randall Balestriero, Facebook AI Research, United States of America; Zichao Wang, Richard Baraniuk, Rice University, United States of America