In this paper, a novel unsupervised model called kernel principal component analysis (KPCA) convolution is proposed for extracting representative features from multi-temporal VHR images. Based on the ...

However, DMF-based approaches face limitations when dealing with complex and nonlinear raw data. To address this issue, Auto-weighted Multi-view Deep Nonnegative Matrix Factorization with Multi-kernel ...

A digital filter based on non-negative matrix factorization (NMF) enables blind deconvolution of temporal information from large data sets, simultaneously recovering both photon arrival times and the ...

Judging and identifying biological activities and biomarkers inside tissues from imaging features of diseases is challenging, so correlating pathological image data with genes inside organisms is of ...

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The SEM images also showed that the presence of TiB 2 and SiC particles helped prevent uncontrolled dendrite growth by pushing the reinforcements into the eutectic liquid, leading to stronger ...