Results 41 to 50 of about 375,641 (168)
Randomized Dimensionality Reduction for k-means Clustering [PDF]
, 2013 We study the topic of dimensionality reduction for $k$-means clustering. Dimensionality reduction encompasses the union of two approaches: \emph{feature selection} and \emph{feature extraction}.
Boutsidis, Christos +3 more
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Dimensionality Reduction in Gene Expression Data Sets
IEEE Access, 2019 Dimensionality reduction is used in microarray data analysis to enhance prediction quality, reduce computing time, and construct more robust models.
Jovani Taveira De Souza +2 more
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Detecting Adversarial Examples through Nonlinear Dimensionality Reduction [PDF]
, 2019 Deep neural networks are vulnerable to adversarial examples, i.e., carefully-perturbed inputs aimed to mislead classification. This work proposes a detection method based on combining non-linear dimensionality reduction and density estimation techniques.
Bacciu, Davide +2 more
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Non‐linear dimensionality reduction using fuzzy lattices
IET Computer Vision, 2013 The proposed method is based on extraction of non‐linearity from the nearest neighbourhood elements of image. To detect non‐linearity, relation between the nearest neighbourhood elements of the image, have been expressed in terms of Gaussian membership ...
Rajiv Kapoor, Rashmi Gupta
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Nonlinear Dimensionality Reduction Based on HSIC Maximization
IEEE Access, 2018 Hilbert-Schmidt independence criterion (HSIC) is typically used to measure the statistical dependence between two sets of data. HSIC first transforms these two sets of data into two reproducing Kernel Hilbert spaces (RKHS), respectively, and then ...
Zhengming Ma +3 more
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Morphological mapping for non‐linear dimensionality reduction
IET Computer Vision, 2015 Recently, much research has been carried out on dimensionality reduction techniques that summarise a large set of features into a smaller set, leading to much less redundancy.
Rajiv Kapoor, Rashmi Gupta
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Subspace clustering of dimensionality-reduced data
, 2014 Subspace clustering refers to the problem of clustering unlabeled high-dimensional data points into a union of low-dimensional linear subspaces, assumed unknown.
Bölcskei, Helmut +2 more
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Reduction algorithm based on supervised discriminant projection for network security data
Tongxin xuebao, 2021 In response to the problem that for dimensionality reduction, traditional manifold learning algorithm did not consider the raw data category information, and the degree of clustering was generally at a low level, a manifold learning dimensionality ...
Fangfang GUO +3 more
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Isometric sketching of any set via the Restricted Isometry Property [PDF]
, 2015 In this paper we show that for the purposes of dimensionality reduction certain class of structured random matrices behave similarly to random Gaussian matrices. This class includes several matrices for which matrix-vector multiply can be computed in log-
Oymak, Samet +2 more
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Dimensionality Reduction Algorithms on High Dimensional Datasets
Emitter: International Journal of Engineering Technology, 2014 Classification problem especially for high dimensional datasets have attracted many researchers in order to find efficient approaches to address them. However, the classification problem has become very complicatedespecially when the number of possible ...
Iwan Syarif
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