Results 71 to 80 of about 64,517 (265)

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

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

Partial depletion of plasminogen activator inhibitor‐1 decreases subcutaneous fat cell hypertrophy and liver cholesterol in high‐fat‐fed female mice

FEBS Letters, EarlyView.
Obesity raises blood levels of PAI‐1, a protein linked to metabolic dysfunction‐associated steatotic liver disease in people with obesity. In female mice fed a high‐fat diet, partially lowering PAI‐1 led to smaller subcutaneous fat cells and lower liver cholesterol, without changing body weight or insulin sensitivity.
Claudia E. Ramirez Bustamante, Robert Sharp, Pradip K. Saha, Elizabeth S. Anaya, Elizabeth L. Lieu, Evelyn L. de Groot, Pooja Patel, Mi Jeong Heo, Aaron R. Cox, Kang Ho Kim, Sean M. Hartig   +10 more
wiley   +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

PARALLEL SPATIOTEMPORAL SPECTRAL CLUSTERING WITH MASSIVE TRAJECTORY DATA [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017
Massive trajectory data contains wealth useful information and knowledge. Spectral clustering, which has been shown to be effective in finding clusters, becomes an important clustering approaches in the trajectory data mining.
Y. Z. Gu, K. Qin, K. Qin, Y. X. Chen, M. X. Yue, T. Guo   +5 more
doaj   +1 more source

Engineered extracellular vesicles enriched with the miR‐214/199a cluster enhance the efficacy of chemotherapy in ovarian cancer

Molecular Oncology, EarlyView.
Loss of the miR‐214/199a cluster is associated with recurrence in ovarian cancer. Engineered small extracellular vesicles (m214‐sEVs) elevate miR‐214‐3p/miR‐199a‐5p in tumor cells, suppress β‐catenin, TLR4, and YKT6 signaling, reprogram tumor‐derived sEV cargo, reduce chemoresistance and migration, and enhance carboplatin efficacy and survival in ...
Weida Wang, Ayesha Alvero, Yi Qin, Mingjin Wang, Alexandra Fox, Yanfeng Li, Michael Millman, Amy Kemper, Gil Mor, Xian Shuang Liu, Michael Chopp, Zheng Gang Zhang, Yi Zhang   +12 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

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

Differential expression of cancer‐related genes supports prediction of poor response to first‐line treatments in T‐ALL pediatric patients with high minimal residual disease

Molecular Oncology, EarlyView.
In the present work, we have identified a transcriptional signature based on the differential expression of six genes (BCL2&MAST4, HSH2D&LAT2, METRN&PITPNM2) that would facilitate the early detection of T‐cell acute lymphoblastic leukemia (T‐ALL) patients prone to a poor treatment response and could be implemented at diagnosis, along with other risk ...
Antonio Lahera, Laura Vela‐Martín, Pablo Fernández‐Navarro, José Luis López‐Lorenzo, Javier Cornago, Pilar Llamas, Eduardo Pérez‐Gómez, José Luis Marín‐Rubio, Manuel Fresno, Javier Santos, José Fernández‐Piqueras, María Villa‐Morales   +11 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