Results 21 to 30 of about 1,974,250 (242)
Algorithms, 2020 Two new initialization methods for K-means clustering are proposed. Both proposals are based on applying a divide-and-conquer approach for the K-means‖ type of an initialization strategy. The second proposal also uses multiple lower-dimensional subspaces
Joonas Hämäläinen +2 more
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Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data
Applied Sciences, 2021 Machine learning has been proven to be effective in various application areas, such as object and speech recognition on mobile systems. Since a critical key to machine learning success is the availability of large training data, many datasets are being ...
Hyeongmin Cho, Sangkyun Lee
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Hyperspectral Remote Sensing Image Classification Based on Partitioned Random Projection Algorithm
Remote Sensing, 2022 Dimensionality reduction based on random projection (RP) includes two problems, namely, the dimensionality is limited by the data size and the class separability of the dimensionality reduction results is unstable due to the randomly generated projection
Shuhan Jia, Quanhua Zhao, Yu Li
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IEEE Access, 2018 Random projection based on compressed sensing can reduce the amount of data transmitted in a wireless sensor network (WSN), and efficient routing can reduce the network traffic. Thus, this paper presents a Random projection-Polar coordinate-Chain routing
Jianhua Qiao, Xueying Zhang
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Tensor Completion Method Based on Coupled Random Projection [PDF]
Jisuanji kexue, 2021 In modern signal processing,the date with large scale,high dimension and complex structure need to be stored and analyzed in more and more fields.Tensors,as a high-order extension of vectors and matrices,can more intuitively represent the structure of ...
YANG Hong-xin, SONG Bao-yan, LIU Ting-ting, DU Yue-feng, LI Xiao-guang
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Towards large scale continuous EDA: a random matrix theory perspective [PDF]
, 2013 Estimation of distribution algorithms (EDA) are a major branch of evolutionary algorithms (EA) with some unique advantages in principle. They are able to take advantage of correlation structure to drive the search more efficiently, and they are able to ...
Kabán, Ata +5 more
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Randomised multichannel singular spectrum analysis of the 20th century climate data [PDF]
Tellus: Series A, Dynamic Meteorology and Oceanography, 2015 In this article, we introduce a new algorithm called randomised multichannel singular spectrum analysis (RMSSA), which is a generalisation of the traditional multichannel singular spectrum analysis (MSSA) into problems of arbitrarily large dimension ...
Teija Seitola +2 more
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Finding projection in the two-stage supply chain in DEA-R with random data using (CRA) model [PDF]
Big Data and Computing Visions, 2021 Data envelopment analysis based on mathematical programming for decision-making units determines the efficiency score in addition to the projection of inefficient DMUs on the efficient frontier.
Mohammad Reza Mozaffari, Sahar Ostovan
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Random projection forest initialization for graph convolutional networks
MethodsX, 2023 Graph convolutional networks (GCNs) were a great step towards extending deep learning to graphs. GCN uses the graph G and the feature matrix X as inputs. However, in most cases the graph G is missing and we are only provided with the feature matrix X. To
Mashaan Alshammari +3 more
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CoRR, 2019 Random projections (RP) are a popular tool for reducing dimensionality while preserving local geometry. In many applications the data set to be projected is given to us in advance, yet the current RP techniques do not make use of information about the data.
Nick Ryder, Zohar S. Karnin, Edo Liberty
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