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In this paper we propose a new clustering technique called denoising cluster analysis (DECLU). We first manually incorporate a small amount of noise among the ...
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Apr 5, 2021 · We contend that denoising and clustering are not alternatives. Rather, they are complementary and both should be used together in COI metabarcoding pipelines.
Denoising results in the detection of putatively correct sequences to which the reads of putatively incorrect sequences are merged (leading to a reduced dataset) ...
ABSTRACT. Spectral Clustering is a popular clustering technique, which is es- pecially effective for complex shaped data. One drawback however.
In this paper, we propose K-LLD: a patch-based, locally adaptive denoising method based on clustering the given noisy image into regions of similar geometric ...
Apr 5, 2021 · We contend that denoising and clustering are not alternatives. Rather, they are complementary and both should be used together in COI metabarcoding pipelines.
Noise should be removed from images so that the details of image objects (e.g., blood vessels, inner foldings, or tumors in human brain) can be clearly seen, ...
Aug 26, 2024 · We present a novel denoising algorithm specifically designed for TEM data. The denoising method uses locally weighted linear regression (LWLR) to predict noise ...
Jun 13, 2023 · Data preprocessing is a simple and effective way to reduce noise and outliers in cluster analysis. This can involve cleaning, normalizing, ...
We propose a single-cell deep clustering model via a dual denoising autoencoder with bipartite graph ensemble clustering called scBGEDA, to identify specific ...