In this paper, a cluster validity concept from an unsupervised to a supervised manner is presented. Most cluster validity criterions were established in an unsupervised manner, although many clustering methods performed in supervised and semi-supervised environments that used context information and performance results of the model. Context-based clustering methods can divide the input spaces u...

 SIR-C quad-pol MLC data acquired in 1994 and ALOS PALSAR quad-pol and dual pol SLC data acquired in 2006 and 2007 over several Indian sites have been processed using PolSARpro software for classification of various land features. The land features include ocean, clear water, settlements, agriculture fields, arid lands, grown and young forest, hilly terrain, mangrove forest, etc. Both unsuperv...

The use of unsupervised data in addition to supervised data has lead to a significant improvement when training discriminative neural networks. However, the best results were achieved with a training process that is divided in two parts: first an unsupervised pre-training step is done for initializing the weights of the network and after these weights are refined with the use of supervised data...

Classification methods are commonly divided into two categories: unsupervised and supervised. Unsupervised methods have the ability to discover new classes by grouping data into clusters or tree structures without using the class labels, but they carry the risk of producing noninterpretable results. On the other hand, supervised methods always find decision rules that discriminate samples with ...

Direct and indirect ecological impacts of roads and their expansion are well documented: habitat degradation, ecosystem fragmentation, changes in natural drainage systems and water quality. Important landscape scale impacts concern roads about direct and indirect effects on deforestation and land use land cover change, that lead to habitat loss and fragmentation, edge effect, resources exploita...

We propose an automatic unsupervised cell event detection and classification method, which expands convolutional Long Short-Term Memory (LSTM) neural networks, for cellular events in cell video sequences. Cells in images that are captured from various biomedical applications usually have different shapes and motility, which pose difficulties for the automated event detection in cell videos. Cur...

Topic model is a popular tool for visual concept learning. Most topic models are either unsupervised or fully supervised. In this paper, to take advantage of both limited labeled training images and rich unlabeled images, we propose a novel regularized Semi-Supervised Latent Dirichlet Allocation (r-SSLDA) for learning visual concept classifiers. Instead of introducing a new complex topic model,...

The purpose of this study is to map wetlands using multi-polarimetric and polarimetric analysis of ALOS Palsar data in coastal North Carolina. Radar sensor offers reliable acquisition of wetlands which are mandated, by Congress, to be mapped every ten years. Radar data products were generated using Wishart supervised (HH-HVVV) and Wishart unsupervised (entropy and dominant scattering mechanism)...

For the purpose of gene identification, we propose an approach to gene expression data mining that uses a combination of unsupervised and supervised learning techniques to search for useful patterns in the data. The approach involves validation and elimination of irrelevant data, extensive data pre-processing, data visualization, exploratory clustering, pattern recognition and model summarizati...