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researcher

R. Chan

4 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG2
  • eess.IV2
same name
  • R. Chan — 6 papers, h 22
  • R. Chan — 3 papers
  • R. Chan — 2 papers
  • R. Chan — 1 paper, h 4
  • R. Chan — 1 paper, h 13

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedDeep Tensor CCA for Multi-view Learning

4 citations · 8 across the 4 of their papers we have counts for

collaborators

4 papers

eess.IV2020

Point Spread Function Engineering for 3D Imaging of Space Debris using a Continuous Exact l0 Penalty (CEL0) Based Algorithm

Chao Wang, Raymond H. Chan, Robert J. Plemmons +1

We consider three-dimensional (3D) localization and imaging of space debris from only one two-dimensional (2D) snapshot image. The technique involves an optical imager that exploit…

cs.LG2020★ 4 cited

Deep Tensor CCA for Multi-view Learning

Hok Shing Wong, Li Wang, Raymond Chan +1

We present Deep Tensor Canonical Correlation Analysis (DTCCA), a method to learn complex nonlinear transformations of multiple views (more than two) of data such that the resulting…

cs.LG2019★ 2 cited

Large-Scale Semi-Supervised Learning via Graph Structure Learning over High-Dense Points

Zitong Wang, Li Wang, Raymond Chan +1

We focus on developing a novel scalable graph-based semi-supervised learning (SSL) method for a small number of labeled data and a large amount of unlabeled data. Due to the lack o…

eess.IV2019★ 2 cited

Total Variation and Tight Frame Image Segmentation with Intensity Inhomogeneity

Raymond Chan, Hongfei Yang, Tieyong Zeng

Image segmentation is an important task in the domain of computer vision and medical imaging. In natural and medical images, intensity inhomogeneity, i.e. the varying image intensi…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.