DeepAI AI Chat

Conditional Manifold Learning

11/26/2021

∙

by Anh Tuan Bui, et al.

∙

∙

This paper addresses a problem called "conditional manifold learning", which aims to learn a low-dimensional manifold embedding of high-dimensional data, conditioning on auxiliary manifold information. This auxiliary manifold information is from controllable or measurable conditions, which are ubiquitous in many science and engineering applications. A broad class of solutions for this problem, conditional multidimensional scaling (including a conditional ISOMAP variant), is proposed. A conditional version of the SMACOF algorithm is introduced to optimize the objective function of conditional multidimensional scaling.

page 1

page 2

page 3

page 4

∙ 10/22/2018

Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning

One of the common tasks in unsupervised learning is dimensionality reduc...

0 Ery Arias-Castro, et al. ∙

∙ 11/16/2017

Parametric Manifold Learning Via Sparse Multidimensional Scaling

We propose a metric-learning framework for computing distance-preserving...

0 Gautam Pai, et al. ∙

∙ 10/26/2022

Bayesian Hyperbolic Multidimensional Scaling

Multidimensional scaling (MDS) is a widely used approach to representing...

0 Bolun Liu, et al. ∙

∙ 06/26/2019

No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms

Nonlinear embedding manifold learning methods provide invaluable visual ...

0 Max Vladymyrov, et al. ∙

∙ 05/24/2019

Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information

Dimensionality reduction and manifold learning methods such as t-Distrib...

0 Bo Kang, et al. ∙

∙ 07/06/2022

Unsupervised Manifold Alignment with Joint Multidimensional Scaling

We introduce Joint Multidimensional Scaling, a novel approach for unsupe...

0 Dexiong Chen, et al. ∙

∙ 03/09/2016

megaman: Manifold Learning with Millions of points

Manifold Learning is a class of algorithms seeking a low-dimensional non...

0 James McQueen, et al. ∙