AI Chat AI Image Generator AI Video Text to Speech

Dimensionality Reduction for k-means Clustering

07/26/2020

∙

by Neophytos Charalambides, et al.

∙

∙

We present a study on how to effectively reduce the dimensions of the k-means clustering problem, so that provably accurate approximations are obtained. Four algorithms are presented, two feature selection and two feature extraction based algorithms, all of which are randomized.

page 1

page 2

page 3

page 4

research

∙ 08/07/2020

Review of Swarm Intelligence-based Feature Selection Methods

In the past decades, the rapid growth of computer and database technolog...

0 Mehrdad Rostami, et al. ∙

research

∙ 02/15/2021

DAC: Deep Autoencoder-based Clustering, a General Deep Learning Framework of Representation Learning

Clustering performs an essential role in many real world applications, s...

16 Si Lu, et al. ∙

research

∙ 10/17/2019

Deep clustering with concrete k-means

We address the problem of simultaneously learning a k-means clustering a...

14 Boyan Gao, et al. ∙

research

∙ 11/21/2010

Random Projections for k-means Clustering

This paper discusses the topic of dimensionality reduction for k-means c...

0 Christos Boutsidis, et al. ∙

research

∙ 01/25/2019

Comparing of Term Clustering Frameworks for Modular Ontology Learning

This paper aims to use term clustering to build a modular ontology accor...

0 Ziwei Xu, et al. ∙

research

∙ 06/07/2021

Evaluating Meta-Feature Selection for the Algorithm Recommendation Problem

With the popularity of Machine Learning (ML) solutions, algorithms and d...

0 Geand Trindade Pereira, et al. ∙

research

∙ 12/02/2017

PFAx: Predictable Feature Analysis to Perform Control

Predictable Feature Analysis (PFA) (Richthofer, Wiskott, ICMLA 2015) is ...

0 Stefan Richthofer, et al. ∙