首先, manifold learning的一个基本假设是，数据在manifold上，而manifold上足够小的区域近似于tangent spac...
In this paper, we propose a novel method, called invertible manifold learning (inv-ML), to tackle this problem. A locally isometric smoothness (LIS) constraint for preserving local geometry is applied to a two-stage inv-ML algorithm. Firstly, a homeomorphic sparse coordinate transformation is ...
Manifold learning (ML) is a research topic of great interest in the field of machine learning that aims to determine the appropriate low-dimensional embeddings of data. The embeddings should preserve the intrinsic structure of the data manifold. Many ML techniques have been proposed to learn the ...
Maximum Likelihood Estimation of Intrinsic Dimension (https://www.stat.berkeley.edu/~bickel/mldim...
Manifold learning is an approach to non-linear dimensionality reduction. Algorithms for this task are based on the idea that the dimensionality of many data sets is only artificially high. Manifold是一种非线性降维的方法。这个任务的算法是基于这样一种想法,即许多数据集的维数只是人为地偏高。
In many cases, the intrinsic dimension of this manifold is low but the representation dimension of the data points is high. To ease data processing requirements, manifold learning (ML) techniques can be used to reduce a high dimensional manifold (HDM) to a low dimensional one while keeping ...
http://mlsp2012.conwiz.dk/fileadmin/lectures/mlsp2012_raich.pdfMLSP2012 Tutorial: Manifold Learning: Modeling and. Algorithms Additional Tutorials http://www2.imm.dtu.dk/projects/manifold/Syllabus.htmlSummer School on Manifold Learning in Image and Signal Analysis ...
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Uniform Manifold Approximation and Projection for Dimension Reduction}", journal = {ArXiv e-prints}, archivePrefix = "arXiv", eprint = {1802.03426}, primaryClass = "stat.ML", keywords = {Statistics - Machine Learning, Computer Science - Computational Geometry, Computer Science - Learning}, yea...
In this paper, a channel estimation algorithm for millimeter wave communication system based on Manifold Learning Extreme Learning Machine (ML-ELM) is proposed. Particularly, the proposed algorithm uses the manifold learning to reduce the characteristic dimension of the received signal, such that the ...