In this paper, we present a summary of our ongoing research on the classification of hyperspectral images. We are experimenting with both supervised and unsupervised algorithms. In particular, we have developed an unsupervised classification algorithm based on Independent Component Analysis (ICA). This algorithm is known as the ICA mixture model (ICAMM) algorithm and has shown promising results...

Recently, multilayer bootstrap network (MBN) has demonstrated promising performance in unsupervised dimensionality reduction. It can learn compact representations in standard data sets, i.e. MNIST and RCV1. However, as a bootstrap method, the prediction complexity of MBN is high. In this paper, we propose an unsupervised model compression framework for this general problem of unsupervised boots...

In paper, LANDSAT multispectral image is classified using several unsupervised and supervised techniques. Pixel-by-pixel classification approaches proved to be infeasible as well as time consuming in case of multispectral images. To overcome this, instead of classifying each pixel, feature based classification approach is used. Three supervised techniques namely, k-NN, BPNN and PCNN are investi...

This paper presents a comparative account of unsupervised and supervised learning models and their pattern classification evaluations as applied to the higher education scenario. Classification plays a vital role in machine based learning algorithms and in the present study, we found that, though the error back-propagation learning algorithm as provided by supervised learning model is very effi...

conclusions in this study, we proposed a new computer aided diagnostic tool for the detection and classification of breast cancer. the obtained results showed that the proposed method is more reliable in diagnostic to assist the radiologists in the detection of abnormal data and to improve the diagnostic accuracy. results after classification with the ensemble supervised algorithm, the performa...

Two major challenging issues arise in unsupervised classification. One is how to generate desired knowledge directly from the data in an unsupervised manner. The other is how to find an appropriate follow-up classifier to use the obtained unsupervised knowledge to perform supervised classification. This paper presents a new approach to unsupervised classification for multispectral imagery. To a...

Supervised and unsupervised categorization have been studied in separate research traditions. A handful of studies have attempted to explore a possible convergence between the two. The present research builds on these studies, by comparing the unsupervised categorization results of Pothos et al. ( 2011 ; Pothos et al., 2008 ) with the results from two procedures of supervised categorization. In...