… of clusters in advance. The core of the algorithm is to construct a clustering feature (CF) tree
for hierarchical clustering, where CF is a triple that summarizes the information of the cluster. …

… framework for hyperspectral imagery classification,” IEEE Trans. Cybern., vol. 48, no. …
clustering for hyperspectral image band selection,” IEEE Trans. Geosci. Remote Sens., vol. …

… attempt to use ECC in the HSI analysis framework. ECC initially uses a basic … a hierarchical
clustering algorithm (HESSC) that analyzes HSIs in a robust and fast manner. A hierarchical …

… plays a vital role to enhance the performance while processing the Hyperspectral images. …
the neural network framework. Auto Encoder based dimensionality reduction is proposed for …

… (GE) framework helps to redefine most DR algorithms in a unified framework, it characterizes
… The GE framework aims to represent a graph in a low dimensional space which preserves …

… The dimensionality reduction methods of HSI is generally … framework are given, including
hyperbolic band hierarchy, … utilized to perform a hierarchical clustering and the total band …

… Therefore, dimensionality reduction can be applied as a … on hierarchical clustering algorithm
in order to preserve the physical meaning of hyperspectral data ie, keep the initial spectral-…

… One popular method for HSI classification is to first use dimension reduction and then follow
… among spectral bands, many feature extraction, band selection and dimension reduction …

… sets of anchor points using a randomized hierarchical clustering method. Then, within each
set … . As a result, the computational complexity of spectral clustering in our framework is linear …

… We introduce an approach to divisive hierarchical clustering that is capable of identifying
clusters in nonlinear manifolds. This approach uses the isometric mapping (Isomap) to …