Self-organizing Maps for Dimensionality Reduction and Image Clustering

A key method in unsupervised neural network is known as Self-Organizing Maps (SOM). The principle aim of SOM is to do a dimensionality reduction. In this report, three tasks would be investigated using SOM

Task 1

This task would be divided to two parts. First part would be by using one dimensional SOM. it would be applied on the data provided and to find an optimal hyper-parameter a grid-search were applied to the model (see table 1). And then it can be evaluated by the output shape (see figure 1).

As highlighted in Figure 1, An appropriate parameter for this dataset occur with the fourth parameter, when choosing 10 neurons(units), 15000 training steps, 0.02 learning rate and 4 as a radius. The second part of this task is to implement and find an appropriate hyper-parameter for 2 dimensional SOM. A grid-search is applied to find a suitable hyper-parameter (see table 2). And then it was evaluated by it is shape (see figure 2).

As highlighted in Figure 2, An appropriate parameter for this dataset occur with the fourth, when choosing 15 neurons(units) along width, 20 neurons(units) along height, 15000 training steps, 0.01 learning rate and 9 as a radius.

Task 2

SOM will be applying on a images dataset using sophisticated SOM Toolbox [1] to divide the dataset into group of images (clusters) based on their similarity. After training the SOM, unified distance matrix (U-matrix) were generated which is a useful way to visualize the data in 2-diemsonal space. To be more precise, U-matrix helps to visualize the distance between the neurons and describes neurons structure. The U-Matrix for this experiment (see images 2), the color describes the distance between nodes, the more the color is near to yellow the more distance between nodes and vice versa . Multiple parameters have been tried and the most suitable one was the same as the one suggested in the documentation [2] (see image 3). 11 neurons weight, 6 neurons height, radius 16, training length 20000.

After visualizing the images in each cluster, the model was clustering most of the similar images in the same cluster. However, there are invalid matches images and that might be due to the data augmentation. However, although there are invalid matches, when comparing the results with untrained SOM, the trained model was doing better.

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