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搜索结果: 1-15 共查到Dimensionality Reduction相关记录20条 . 查询时间(0.044 秒)
Differential computation analysis (DCA) is a technique recently introduced by Bos et al. and Sanfelix et al. for key recovery from whitebox implementations of symmetric ciphers. It consists in applyin...
Reducing the dimensionality of the measurements is an important problem in side-channel analysis. It allows to capture multi-dimensional leakage as one single compressed sample, and therefore also hel...
Multi-variate side-channel attacks allow to break higher-order masking protections by combining several leakage samples. But how to optimally extract all the information contained in all possible d-...
A dimensionality reduction algorithm based on feature extraction of the spectral curve using fractal analysis which considering both the spatial characteristic and spectral characteristic of hyper spe...
We present a unified duality view of several recently emerged spectral methods for nonlinear dimensionality reduction, including Isomap, locally linear embedding, Laplacian eigenmaps, and maximum vari...
Dimensionality Reduction for Speech Recognition Using Neighborhood Components Analysis.
The processing of hyperspectral remote sensing data, for information retrieval, is challenging due to its higher dimensionality. Machine learning based algorithms such as Support Vector Machine (SVM) ...
This paper combine two conventional feature extraction methods (NWFE&NPE) in a novel framework and present a new semi-supervised feature extraction method called Adjusted Semi supervised Discriminant ...
In recent years, manifold learning has become increasingly popular as a tool for performing non-linear dimensionality reduction. This has led to the development of numerous algorithms of varying degre...
Fat quality is determined by the composition of fatty acids. Genetic relationships between this composition and single nucleotide polymorphisms (SNPs) in the stearoyl-CoA desaturase1 (SCD1) gene were ...
A numerical method is proposed to approximate the inverse of a general bi-Lipschitz nonlinear dimensionality reduction mapping, where the forward and consequently the inverse mappings are only explici...
Over the past few decades, dimensionality reduction has been widely exploited in computer vision and pattern analysis.This paper proposes a simple but effective nonlinear dimensionality reduction algo...
A collaborative convex framework for factoring a data matrix $X$ into a non-negative product $AS$, with a sparse coefficient matrix $S$, is proposed. We restrict the columns of the dictionary matrix $...
High-dimensional classification has become an increasingly important problem. In this paper we propose a "Multivariate Adaptive Stochastic Search" (MASS) approach which first reduces the dimension of...

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