Unsupervised feature dimension reduction for classification of MR spectra
نویسندگان
چکیده
منابع مشابه
Unsupervised feature dimension reduction for classification of MR spectra.
We present an unsupervised feature dimension reduction method for the classification of magnetic resonance spectra. The technique preserves spectral information, important for disease profiling. We propose to use this technique as a preprocessing step for computationally demanding wrapper-based feature subset selection. We show that the classification accuracy on an independent test set can be ...
متن کاملUnsupervised 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 کاملFeature-aware Label Space Dimension Reduction for Multi-label Classification
Label space dimension reduction (LSDR) is an efficient and effective paradigm for multi-label classification with many classes. Existing approaches to LSDR, such as compressive sensing and principal label space transformation, exploit only the label part of the dataset, but not the feature part. In this paper, we propose a novel approach to LSDR that considers both the label and the feature par...
متن کاملFeature Reduction for Unsupervised Learning
In this project, four unsupervised feature reduction algorithms for clustering problem were investigated and experimented upon two sets of data – handwritten digits data set and the functional magnetic resonance imaging (fMRI) resting state data set. Ratio of sum of squares (RSS), leverage score (LEV), and Laplacian score (LAP) were used to rank the influences of the features in the clustering....
متن کاملUnsupervised Locally Linear Embedding for Dimension Reduction
In this paper, Locally Linear Embedding (LLE) has been implemented for unsupervised non-linear dimension reduction that computes low dimensional, neighborhood preserving embeddings of high dimensional data. Inputs are mapped into a single global coordinate system of lower dimensionality, and its optimizations though capable of generating highly nonlinear embeddings but local minima are not invo...
متن کاملذخیره 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: Magnetic Resonance Imaging
سال: 2004
ISSN: 0730-725X
DOI: 10.1016/j.mri.2003.08.033