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Nearest neighbor classifier (NNC), a non-parametric pattern classification technique is not only simple to use, but often shows good performance. It is used in several domains, like data mining, machine learning, image/video/audio data analysis and retrieval, etc. It has some shortcomings or limitations, like it can be biased due to curse of dimensionality effect, huge computational requirements, etc. With large number of training instances it can achieve a better classification accuracy. But, this can worse the computational burden. The monograph presents a series of techniques whereby...

Hyperspectral image classification is the most popular research area in the hyperspectral community and has attracted significant interest in remote sensing. HSI classification is a challenging task because of the large dimensionality of the data, inadequate datasets, huge data, and limited training samples. Several Deep Learning (DL) based architectures are being explored to resolve the aforementioned challenges and provide significant improvements in HSI data analysis. Limited studies have been presented in the literature in the direction of exploring deep learning architectures for joint...

Fuzzy C-means and kernel FCM-F are multi scan methods and require NC distance computations where N is the size of the dataset D, C is the number of cluster centers in the data and Kernel FCM-K also is a multi scan method requiring N2C distance computations in each iteration. For large values of N, the overall computation cost will go on increasing for these methods. The Book proposed two-step prototype based hybrid techniques to speed-up FCM, KFCM-F and KFCM-K. The proposed algorithms are called Prototype based FCM (PFCM), Prototype based KFCM- F (PKFCM-F) and Prototype based KFCM-K(PKFCM-K)....