Advances in neural information processing systems
H Lyu, N Sha, S Qin, M Yan, Y Xie… - Advances in neural …, 2019 - par.nsf.gov
This paper extends robust principal component analysis (RPCA) to nonlinear manifolds.
Suppose that the observed data matrix is the sum of a sparse component and a component …
Suppose that the observed data matrix is the sum of a sparse component and a component …
Supervised capacity preserving mapping: a clustering guided visualization method for scRNA-seq data
Motivation The rapid development of scRNA-seq technologies enables us to explore the
transcriptome at the cell level on a large scale. Recently, various computational methods …
transcriptome at the cell level on a large scale. Recently, various computational methods …
Reconstruction of line-embeddings of graphons
Consider a random graph process with n vertices corresponding to points vi∼ iid Unif [0, 1]
embedded randomly in the interval, and where edges are inserted between vi, vj …
embedded randomly in the interval, and where edges are inserted between vi, vj …
Manifold denoising by nonlinear robust principal component analysis
This paper extends robust principal component analysis (RPCA) to nonlinear manifolds.
Suppose that the observed data matrix is the sum of a sparse component and a component …
Suppose that the observed data matrix is the sum of a sparse component and a component …
A multidimensional scaling and sample clustering to obtain a representative subset of training data for transfer learning-based rosacea lesion identification
H Binol, MKK Niazi, A Plotner… - Medical Imaging …, 2020 - spiedigitallibrary.org
Rosacea is a common cutaneous disorder characterized by facial redness, swelling, and
flushing, and it is usually diagnosed by a dermatologist after a visual examination …
flushing, and it is usually diagnosed by a dermatologist after a visual examination …
An exact formula for matrix perturbation analysis and its applications
In this paper, we establish a useful set of formulae for the $\sin\Theta $ distance between the
original and the perturbed singular subspaces. These formulae explicitly show that how the …
original and the perturbed singular subspaces. These formulae explicitly show that how the …
[图书][B] Exploring Low-Rank Prior in High-Dimensional Data
H Lyu - 2023 - search.proquest.com
High-dimensional data plays a ubiquitous role in real applications, ranging from biology,
computer vision, to social media. The large dimensionality poses new challenges on …
computer vision, to social media. The large dimensionality poses new challenges on …
Modified multidimensional scaling and high dimensional clustering
Multidimensional scaling is an important dimension reduction tool in statistics and machine
learning. Yet few theoretical results characterizing its statistical performance exist, not to …
learning. Yet few theoretical results characterizing its statistical performance exist, not to …
MAPPING THE HAPPINESS LEVEL DISPARITY OF THE INDONESIAN POPULATION USING MULTIDIMENSIONAL SCALING
S Sumin, H Retnawati - BAREKENG: Jurnal Ilmu Matematika dan …, 2022 - ojs3.unpatti.ac.id
Abstract The Central Statistics Agency has published a survey report on the happiness of the
Indonesian people in 2017. The survey results show that there are disparities that vary …
Indonesian people in 2017. The survey results show that there are disparities that vary …
Manifold denoising by Nonlinear Robust Principal Component Analysis
R Wang, M Yan, H Lyu, Y Xie, N Sha, S Qin - openreview.net
The paper extends the idea of Robust Principal Component Analysis to nonlinear manifolds.
Suppose the data matrix contains a sparse component and a component drawn from some …
Suppose the data matrix contains a sparse component and a component drawn from some …