Knowing the type of data is widely required to make better and faster decisions. However, the process requires the user to provide information about the configuration of the data. This paper presents the first attempt to analyze data to extract it is type automatically from multi-dimensional data sets. This is useful not only for experts but also to users, also reduces manual search effort. Layers of multi-dimensional data are formed and evaluated, and the focus is on the most efficient ones. Experiments on experimental and real data demonstrate the efficiency and effectiveness of the proposed method.
Hyperspectral image visualization reduces color bands to three, but prevailing linear methods fail to address data characteristics, and nonlinear embeddings are computationally demanding. Qualitative evaluation of embedding is also lacking. We propose faithful stochastic proximity embedding (FSPE), which is a scalable and nonlinear dimensionality reduction method. FSPE considers the nonlinear characteristics of spectral signatures, yet it avoids the costly computation of geodesic distances that are often required by other nonlinear methods. Furthermore, we employ a pixelwise metric that measures the quality of hyperspectral image visualization at each pixel. FSPE outperforms the state-of-art methods by at least 12% on average and up to 25% in the qualitative measure. An implementation on graphics processing units is two orders of magnitude faster than the baseline. Our method opens the path to high-fidelity and real-time analysis of hyperspectral images.
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