Results 101 to 110 of about 74,563 (305)

Robust Spectral Clustering via Matrix Aggregation

IEEE Access, 2018
Spectral clustering has become one of the most popular clustering algorithms in recent years. In real-world clustering problems, the data points for clustering may have considerable noise.
Lei Du, Yan Pan, Xiaonan Luo
doaj   +1 more source

Patent clustering using spectral clustering.

, 2020
Patent clustering using spectral clustering.
Inchae Park (8407338), Byungun Yoon (7571990), Sunhye Kim (3127359)   +2 more
core   +1 more source

CD47 promotes mitogen‐activated protein kinase and epithelial‐to‐mesenchymal transition molecular programs to drive prometastatic phenotypes in non‐small cell lung cancer

Molecular Oncology, EarlyView.
Beyond its role in immune evasion, this study identified that CD47 drives tumor‐intrinsic signaling in non‐small cell lung cancer (NSCLC). Transcriptomic profiling and functional studies revealed that CD47 regulates cell adhesion, migration, and metastasis through an ERK–EMT signaling axis.
Asa P.Y. Lau, Lilian G. Zhai, Ryunosuke Hoshi, Zackary Rousseau, Abraam Zakhary, Yin Fang Wu, Rola M. Saleeb, Heyu Ni, Kelsie L. Thu   +8 more
wiley   +1 more source

Fast kernel spectral clustering [PDF]

Neurocomputing, 2017
Abstract Spectral clustering suffers from a scalability problem in both memory usage and computational time when the number of data instances N is large. To solve this issue, we present a fast spectral clustering algorithm able to effectively handle millions of datapoints at a desktop PC scale.
Langone, Rocco, Suykens, Johan
openaire   +1 more source

Exploring the Potential of Spectral Classification in Estimation of Soil Contaminant Elements

Remote Sensing, 2017
Soil contamination by arsenic and heavy metals is an increasingly severe environmental problem. Efficiently investigation of soil contamination is the premise of soil protection and further the foundation of food security.
Weichao Sun, Xia Zhang, Bin Zou, Taixia Wu   +3 more
doaj   +1 more source

Approximate sparse spectral clustering based on local information maintenance for hyperspectral image classification. [PDF]

PLoS ONE, 2018
Sparse spectral clustering (SSC) has become one of the most popular clustering approaches in recent years. However, its high computational complexity prevents its application to large-scale datasets such as hyperspectral images (HSIs).
Qing Yan, Yun Ding, Jing-Jing Zhang, Li-Na Xun, Chun-Hou Zheng   +4 more
doaj   +1 more source

Metastasis on pause: How dormant tumor cells stay hidden within the tumor microenvironment and evade immune surveillance

Molecular Oncology, EarlyView.
Dormant cancer cells can hide in distant organs for years, evading treatment and the immune system. This review highlights how signals from the surrounding tissue and immune environment keep these cells inactive or trigger their reawakening. Understanding these mechanisms may help develop therapies to eliminate or control dormant cells and prevent ...
Kanishka Tiwary, Jose Javier Bravo‐Cordero   +1 more
wiley   +1 more source

Cover Tree-Optimized Spectral Clustering: Efficient Nearest Neighbor Search for Large-Scale Data Partitioning

Machine Learning and Knowledge Extraction
Spectral clustering has established itself as a powerful technique for data partitioning across various domains due to its ability to handle complex cluster structures.
Abderrafik Laakel Hemdanou, Youssef Achtoun, Sara Mouali, Mohammed Lamarti Sefian, Vesna Šešum Čavić, Stojan Radenović   +5 more
doaj   +1 more source

Flow Enabled Target Capture Halbach‐based magnetic enrichment increases circulating tumor cell capture from blood in metastatic cancer patients

Molecular Oncology, EarlyView.
Pair‐wise comparison of the CellSearch and FETCH enrichment technologies for circulating tumor cells (CTCs) from metastatic breast, prostate, and small cell lung cancer patients shows an increased capture of CTCs using FETCH enrichment. The clinical implementation of circulating tumor cells (CTCs) as a predictive tool for therapy efficacy in the ...
Michiel Stevens, Anouk Mentink, Tom Niessink, Frank Coumans, Leonie Mekenkamp, Ruchi Bansal, Jeroen Hiltermann   +6 more
wiley   +1 more source

One-Step Joint Learning of Self-Supervised Spectral Clustering With Anchor Graph and Fuzzy Clustering for Land Cover Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Spectral clustering, as an algorithm based on graph theory and spectral theory, has shown excellent performance in classification tasks of hyperspectral images in recent years. Although better results have been achieved, some challenges still exist.
Chengmao Wu, Jiale Zhang
doaj   +1 more source