Rank: 569 based on 224 downloads (last 30 days) and 5 files submitted
photo

Laurent S

E-mail
Company/University
KU Leuven
Lat/Long
50.86417, 4.678849

Personal Profile:
Professional Interests:

 

Watch this Author's files

 

Files Posted by Laurent View all
Updated   File Tags Downloads
(last 30 days)
Comments Rating
21 May 2013 Screenshot boxplotstack Displays stacked box plots with labels. Author: Laurent S box plot, stack 26 0
11 Feb 2013 Screenshot k-means++ Cluster multivariate data using the k-means++ algorithm. Author: Laurent S data mining, clustering, kmeans 123 27
  • 4.85714
4.9 | 7 ratings
07 Jul 2011 saveaspdf Save a figure as a clean pdf file ready for publication. Author: Laurent S saveas, saveaspdf, pdf, print 19 3
  • 4.5
4.5 | 2 ratings
06 Feb 2011 Kronecker product An efficient implementation of the Kronecker product for dense, sparse and logical matrices. Author: Laurent S kronecker, kron 27 11
  • 5.0
5.0 | 2 ratings
21 Oct 2010 Khatri-Rao product An efficient implementation of the Khatri-Rao product. Author: Laurent S khatrirao 29 1
Comments and Ratings by Laurent View all
Updated File Comments Rating
15 Sep 2013 k-means++ Cluster multivariate data using the k-means++ algorithm. Author: Laurent S

@Matei Tene: dot(C,C) is actually implemented as sum(C.*conj(C)), making both are equally fast.

08 Feb 2013 k-means++ Cluster multivariate data using the k-means++ algorithm. Author: Laurent S

@Xiaobo Li: Thank you for your comment, I updated the code so that it works with 1D datasets. The update should appear on the File Exchange shortly.

02 Jan 2012 k-means++ Cluster multivariate data using the k-means++ algorithm. Author: Laurent S

@Micky: Good catch :-) Yes, using the distance instead of squared distance shows consistently better results on the datasets used by the original authors. If you happen to find a better heuristic, I would be happy to hear about it of course!

21 Dec 2011 k-means++ Cluster multivariate data using the k-means++ algorithm. Author: Laurent S

@Andreas: Thanks! To answer your question: you could try using PCA on the cluster centers to discover which clusters are the most important (although it depends on how you define 'important').

08 Aug 2011 The complex optimal step-size for Tensor Decomp. New ALS methods with extrapolating search directions and optimal step size Author: Chen ??

Very interesting, I look forward to reading your article.

Comments and Ratings on Laurent's Files View all
Updated File Comment by Comments Rating
27 Jan 2014 k-means++ Cluster multivariate data using the k-means++ algorithm. Author: Laurent S mirko-stifler

I have code:
nfo = imfinfo('im.png');
X = im2double(imread('im','png'));
X = imadjust(X);
k = 4;

and kmeanspp??
I want to output the resulting image

for example:
X=kmeanspp(X,k);

how can I do?

thanks
I am an Italian student

10 Jan 2014 k-means++ Cluster multivariate data using the k-means++ algorithm. Author: Laurent S asmae

dear laurent,
when i use this algorithm several time for the same input, i get different output results ! it's this logic ?!

13 Dec 2013 k-means++ Cluster multivariate data using the k-means++ algorithm. Author: Laurent S Qinfan

Thanks! Very easy to use.

28 Nov 2013 k-means++ Cluster multivariate data using the k-means++ algorithm. Author: Laurent S Ajit

Hi Laurent,

I'm having a bit of trouble understanding the bsxfun lines. Is there any documentation or explanation of why you are maximizing this and how it relates to the distance, can't quite understand where the C' comes from?

Thanks.

22 Oct 2013 k-means++ Cluster multivariate data using the k-means++ algorithm. Author: Laurent S toso, tsan

Hi Laurent,

First of all thanks for the code. I actually trying to use kmeans to bin my 1D data, the C output could be used as the bin centers. However, if I want the function to output edges is there a non-trivial way to do it?

Thanks.

Contact us