Methods for reducing dimensionality of hyperspectral image data having a number of spatial pixels, each associated with a number of spectral dimensions, include receiving sets of coefficients associated with each pixel of the hyperspectral image data, a set of basis vectors utilized to generate the ...
ECE 599 - Midterm Report Dimensionality Reduction for Spectral Clustering with Constraints Clustering is the task of grouping objects into clusters such that similar objects are grouped together. Among various techniques, spectral clustering [5] is one of the most flexible methods that doesn't make ...
In this chapter, we study and put under a common framework a number of non-linear dimensionality reduction methods, such as Locally Linear Embedding, Isomap, Laplacian eigenmaps and kernel PCA, which are based on performing an eigen-decomposition (hence the name “spectral”). That framework also...
A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models We introduce a new perspective on spectral dimensionality reduction which views these methods as Gaussian Markov random fields (GRFs). Our unifying perspec... ND Lawrence∗ - 《Journal of Machine Learnin...
Spectral Dimensionality Reduction In this chapter, we study and put under a common framework a number of non-linear dimensionality reduction methods, such as Locally Linear Embedding, Isomap, Laplacian eigenmaps and kernel PCA, which Y Bengio,O Delalleau,NL Roux,... 被引量: 48发表: 2006年 Ev...
Licciardi, GiorgioChanussot, JocelynEuropean Journal of Remote SensingLicciardi, G., & Chanussot, J. (2018). Spectral transforma- tion based on nonlinear principal component analysis for dimensionality reduction of hyperspectral images. European Journal Remote Sensing, 51, 375-390....
Dimensionality reductionGeneralized kernel formulationKernel PCASpectral clusteringSupport vector machineThis paper presents the development of a unified view of spectral clustering and unsupervised dimensionality reduction approaches within a generalized kernel framework. To do so, the authors propose a multi...
The manifold learning method is a new kind of nonlinear dimensionality reduction approach,which can effectively reduce dimensions for high dimensional data in an intrinsic nonlinear manifold form.Until currently,this kind of method has been successfully applied to many data mining areas such as clusterin...
6.4.5 Dimensionality Reduction Methods for Hyperspectral Images Dimensionality reduction selects spectral components with higher HSI-to-noise ratio (SNR) among neighboring bands with high correlation. Some known techniques are PCA (Jolliffe, 1986); computing KLT, which is the best data representation in...
The Garver-Siegel-Maritorena SOA model is used as a base to test these computational methods. It is observed that 1) LM is the fastest method, but SAA has the largest number of valid retrievals; 2) the quality of final solutions are strongly influenced by the forms of spectral models (...