most citedJoint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction

2 citations · 2 across the 1 of their papers we have counts for

collaborators

2 papers

cs.CV2020★ 2 cited

Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction

Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot +2

Conventional nonlinear subspace learning techniques (e.g., manifold learning) usually introduce some drawbacks in explainability (explicit mapping) and cost-effectiveness (lineariz…

cs.LG2018

Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification

Danfeng Hong, Naoto Yokoya, Jian Xu +1

Despite the fact that nonlinear subspace learning techniques (e.g. manifold learning) have successfully applied to data representation, there is still room for improvement in expla…