W = TSNEM(A,K,N,P,MAX)
W = A*TSNEM(,K,N,P,MAX)
W = A*TSNEM(K,N,P,MAX)
D = B*W
| A|| Dataset or matrix of doubles, used for training the mapping|
| B|| Dataset, same dimensionality as A, to be mapped|
| K|| Target dimension of mapping (default 2) |
| N|| Initial dimension (default 30) |
| P|| Perplexity (default 30) |
| MAX|| Maximum number of iterations, default 1000 |
| W|| Trained mapping|
| D|| 2D dataset|
This is PRTools inteface to the t-SNE Simple Matlab routine for high dimensional data visualisation. The output is a non-linear projection of the original vector space to a K-dimensional target space. The procedure starts with a preprocessing to N dimensions by PCA. The perplexity determines the number of neighbors taken into account, see references.
The test dataset B is mapped on the target space by a linear mapping between the dissimilarity representation of A and the target space. See also multi-dimensional scaling by MDS or SAMMONM.
prdatasets; % make sure prdatasets is in the path
a = satellite; % 36D dataset, 6 classes, 6435 objects
a = gendat(a,0.3); % take a subset to make it faster
[x,y] = gendat(a,0.5); % split in train and test set
w = x*tsnem; % compute mapping
figure; scattern(x*w); % show train set mapped to 2D: looks overtrained
figure; scattern((x+randn(size(x))*1e-5)*w); % some noise helps
figure; scattern(y*w); % show test set mapped to 2D
figure; scattern(y*pca(x,2)); % compare with pca
1. L.J.P. van der Maaten and G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. J. of ML Research, 2579-2605, 2008.
2. L.J.P. van der Maaten. Learning a Parametric Embedding by Preserving Local Structure. Proc. 12th Int. Conf. on AI and Stats. (AI-STATS), JMLR W&CP 5:384-391, 2009.
3. L.J.P. van der Maaten. Barnes-Hut-SNE. Proc. Int. Conf. on Learning Representations.
4. E. Pekalska, D. de Ridder, R.P.W. Duin, and M.A. Kraaijveld, A new method of generalizing Sammon mapping with application to algorithm speed-up, ASCI99, Proc. 5th Annual ASCI Conf., 1999, 221-228. [pdf]
datasets, mappings, pcam, mds, sammonm, scatterd, scattern,
|This file has been automatically generated. If badly readable, use the help-command in Matlab.|