Results 31 to 40 of about 77,553 (343)
Unsupervised Deep Embedded Clustering for High-Dimensional Visual Features of Fashion Images
Applied Sciences, 2023 Fashion image clustering is the key to fashion retrieval, forecasting, and recommendation applications. Manual labeling-based clustering is both time-consuming and less accurate.
Umar Subhan Malhi +5 more
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Curse of Dimensionality for TSK Fuzzy Neural Networks: Explanation and Solutions [PDF]
IEEE International Joint Conference on Neural Network, 2021 Takagi-Sugeno-Kang (TSK) fuzzy system with Gaussian membership functions (MFs) is one of the most widely used fuzzy systems in machine learning. However, it usually has difficulty handling high-dimensional datasets.
Yuqi Cui, Dongrui Wu, Yifan Xu
semanticscholar +1 more source
Variable selection in the curse of dimensionality
, 2016 High-throughput technologies nowadays are leading to massive availability of data to be explored. Therefore, we are keen to build mathematical and statistical meth- ods for extracting as much value from the available data as possible. However, the large dimensionality in terms of both sample size and number of features or variables poses new challenges.
Mihaela A. Mares
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BMC Bioinformatics, 2023 The causes of many complex human diseases are still largely unknown. Genetics plays an important role in uncovering the molecular mechanisms of complex human diseases.
Yixin Zhang, Wei Liu, Weiliang Qiu
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Progress in Microbes and Molecular Biology, 2023 Biological data obtained from sequencing technologies is growing exponentially. Multi-omics data is one of the biological data that exhibits high dimensionality, or more commonly known as the curse of dimensionality.
Nuraina Syaza Azman +6 more
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Locality defeats the curse of dimensionality in convolutional teacher–student scenarios [PDF]
Neural Information Processing Systems, 2021 Convolutional neural networks perform a local and translationally-invariant treatment of the data: quantifying which of these two aspects is central to their success remains a challenge. We study this problem within a teacher–student framework for kernel
Alessandro Favero +2 more
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Theory I: Deep networks and the curse of dimensionality
Bulletin of the Polish Academy of Sciences: Technical Sciences, 2023 Deep Learning references start with Hinton’s backpropagation and with Lecun’s convolutional networks (see for a review [8]). Of course, multilayer convolutional networks have been around at least as far back as the optical processing era of the 1970s ...
T. Poggio, Q. Liao
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Finite-Sample Guarantees for Wasserstein Distributionally Robust Optimization: Breaking the Curse of Dimensionality [PDF]
Operational Research, 2020 Wasserstein distributionally robust optimization is a recent emerging modeling paradigm for decision making under data uncertainty. Because of its computational tractability and interpretability, it has achieved great empirical successes across several ...
Rui Gao
semanticscholar +1 more source
Genetically Optimized UFLANN for Uncovering Clusters
IEEE Access, 2023 In this work, we present a novel clustering approach which is inheriting the best characteristics of Unsupervised Functional Link Artificial Neural Network (UFLANN) and Genetic Algorithms (GAs) for uncovering clusters embedded in dataset represented ...
Himanshu Dutta +4 more
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A feature extraction method based on spectral segmentation and integration of hyperspectral images
International Journal of Applied Earth Observations and Geoinformation, 2020 In response to the curse of dimensionality in hyperspectral images (HSIs), to date, numerous dimensionality reduction methods have been proposed among which the feature extraction (FE) methods are of particular interest.
Sayyed Hamed Alizadeh Moghaddam +2 more
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