Dynamic Negative Sampling Autoencoder for Hyperspectral Anomaly Detection
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2022.3220514
Abstract: Hyperspectral anomaly detection (HAD) aims at detecting the anomalies without any prerequisite information, which gains lots of attention in recent years. Most of existing detectors locate the anomalies by eliminating the background. The background is… read more here.
Keywords: negative sampling; hyperspectral anomaly; dynamic negative; anomaly detection ... See more keywords
Region or Global? A Principle for Negative Sampling in Graph-Based Recommendation
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2022.3155155
Abstract: Graph-based recommendation systems are blossoming recently, which models user-item interactions as a user-item graph and utilizes graph neural networks (GNNs) to learn the embeddings for users and items. A fundamental challenge of graph-based recommendation is… read more here.
Keywords: negative sampling; mml mml; mml; tex math ... See more keywords
Rethinking Collaborative Metric Learning: Toward an Efficient Alternative Without Negative Sampling
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2022.3141095
Abstract: The recently proposed Collaborative Metric Learning (CML) paradigm has aroused wide interest in the area of recommendation systems (RS) owing to its simplicity and effectiveness. Typically, the existing literature of CML depends largely on the… read more here.
Keywords: negative sampling; metric learning; without negative; efficient alternative ... See more keywords
How to balance the bioinformatics data: pseudo-negative sampling
Sign Up to like & getrecommendations! Published in 2019 at "BMC Bioinformatics"
DOI: 10.1186/s12859-019-3269-4
Abstract: Imbalanced datasets are commonly encountered in bioinformatics classification problems, that is, the number of negative samples is much larger than that of positive samples. Particularly, the data imbalance phenomena will make us underestimate the performance… read more here.
Keywords: positive samples; pseudo negative; negative sampling; negative samples ... See more keywords