dc.contributor.author |
Sil, Partha |
|
dc.date.accessioned |
2022-03-25T07:01:29Z |
|
dc.date.available |
2022-03-25T07:01:29Z |
|
dc.date.issued |
2021-07 |
|
dc.identifier.citation |
27p. |
en_US |
dc.identifier.uri |
http://hdl.handle.net/10263/7329 |
|
dc.description |
Dissertation under the supervision of Professor Rajat Kumar De and Professor Smarajit Bose |
en_US |
dc.description.abstract |
Manifold Learning has been widely exploited in the arena of data analysis, machine
learning and pattern recognition. The main assumption behind manifold learning is that
the input high-dimensional data lies intrinsically on a low-dimensional manifold. This technique is to be used for non-linear dimensionality reduction. Although there are very well
known dimensionality reduction techniques are already designed such as Principal Component Analysis (PCA), Independent Component Analysis, Linear Discriminant Analysis, and
others but they are unable to capture the non linear structure of the data so that researchers
are interested in this area. After that many manifold learning algorithms are developed such
as Multidimensional Scaling (MDS), Locally linear embedding (LLE), Hessian Eigenmapping, t-distributed Stochastic Neighbor Embedding (t-SNE) etc. Multidimensional Scaling
is one of them that seeks vectorial representation of the data points given the pairwise
distance between the data points.There are two variant of Multidimensional Scaling one is
metric Multidimensional Scaling and other is non-metric Multidimensional Scaling. Our
interest on metric Multidimensional Scaling. The methodologies that are available to implement classical metric-MDS boil down to finding eigen values and eigen vectors of some
matrix and which is computationally difficult for large dimensional matrix that motivate
us to implement it using neural network setup. We are implementing it using Artificial
Neural Networks and experiment it on famous Iris and Wine datasets and compare our
results with some existing methods on few other datasets also. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Indian Statistical Institute, Kolkata. |
en_US |
dc.relation.ispartofseries |
Dissertation; |
|
dc.subject |
Artificial Neural Networks |
en_US |
dc.subject |
Multidimensional Scaling |
en_US |
dc.subject |
Locally linear embedding |
en_US |
dc.subject |
Sammon mapping |
en_US |
dc.subject |
Dimensional Matrix |
|
dc.title |
Multidimensional Scaling Using Artificial Neural Networks |
en_US |
dc.type |
Other |
en_US |