A Dimension Reduction Framework for HSI Classification Using Fuzzy and Kernel NFLE Transformation

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A Dimension Reduction Framework for HSI Classification Using Fuzzy and Kernel NFLE Transformation

In this paper, a general nearest feature line (NFL) embedding (NFLE) transformation called fuzzy-kernel NFLE (FKNFLE) is proposed for hyperspectral image (HSI) classification in which kernelization and fuzzification are simultaneously considered. Though NFLE has successfully demonstrated its discriminative capability, the non-linear manifold structure cannot be structured more efficiently by li...

متن کامل

Regression on manifolds using kernel dimension reduction

We study the problem of discovering a manifold that best preserves information relevant to a nonlinear regression. Solving this problem involves extending and uniting two threads of research. On the one hand, the literature on sufficient dimension reduction has focused on methods for finding the best linear subspace for nonlinear regression; we extend this to manifolds. On the other hand, the l...

متن کامل

Canonical kernel dimension reduction

A new kernel dimension reduction (KDR) method based on the gradient space of canonical functions is proposed for sufficient dimension reduction (SDR). Similar to existing KDR methods, this new method achieves SDR for arbitrary distributions, but with more flexibility and improved computational efficiency. The choice of loss function in cross-validation is discussed, and a two-stage screening pr...

متن کامل

Unsupervised Kernel Dimension Reduction

We apply the framework of kernel dimension reduction, originally designed for supervised problems, to unsupervised dimensionality reduction. In this framework, kernel-based measures of independence are used to derive low-dimensional representations that maximally capture information in covariates in order to predict responses. We extend this idea and develop similarly motivated measures for uns...

متن کامل

Kernel Regularization and Dimension Reduction

It is often possible to use expert knowledge or other sources of information to obtain dissimilarity measures for pairs of objects, which serve as pseudo-distances between the objects. When dissimilarity information is available as the data, there are two different types of problems of interest. The first is to estimate full position configuration for all objects in a low dimensional space whil...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Remote Sensing

سال: 2015

ISSN: 2072-4292

DOI: 10.3390/rs71114292