Technical Program

MLSP-L5: Metric and Embedding Learning

Session Type: Lecture
Time: Wednesday, March 8, 16:00 - 18:00
Location: Grand Salon 3
Session Chair: Waheed U. Bajwa, Rutgers University, USA
 
MLSP-L5.1: NOISY OBJECTIVE FUNCTIONS BASED ON THE F-DIVERGENCE
         Markus Nussbaum-Thom; International Business Machine
         Ralf Schl├╝ter; RWTH Aachen University
         Vaibhava Goel; International Business Machine
         Hermann Ney; RWTH Aachen University
 
MLSP-L5.2: EMBEDDED CLUSTERING VIA ROBUST ORTHOGONAL LEAST SQUARE DISCRIMINANT ANALYSIS
         Rui Zhang; Northwestern Polytechnical University
         Feiping Nie; Northwestern Polytechnical University
         Xuelong Li; Northwestern Polytechnical University
 
MLSP-L5.3: A GEOMETRIC LEARNING APPROACH ON THE SPACE OF COMPLEX COVARIANCE MATRICES
         Hatem Hajri; IMS Bordeaux
         Salem Said; IMS Bordeaux
         Lionel Bombrun; IMS Bordeaux
         Yannick Berthoumieu; IMS Bordeaux
 
MLSP-L5.4: DENSITY RIDGE MANIFOLD TRAVERSAL
         Jonas Nordhaug Myhre; Uit - The arctic university of Norway
         Michael Kampffmeyer; Uit - The arctic university of Norway
         Robert Jenssen; Uit - The arctic university of Norway
 
MLSP-L5.5: POWER-LAW STOCHASTIC NEIGHBOR EMBEDDING
         Huan-Hsin Tseng; University of Michigan Health System
         Issam El Naqa; University of Michigan Health System
         Jen-Tzung Chien; National Chiao Tung University
 
MLSP-L5.6: LARGEST CENTER-SPECIFIC MARGIN FOR DIMENSION REDUCTION
         Jian'an Zhang; Northwestern Polytechnical University
         Yuan Yuan; Northwestern Polytechnical University
         Feiping Nie; Northwestern Polytechnical University
         Qi Wang; Northwestern Polytechnical University