Clustering Large Applications LMDRT: Logarithmic Marginal Density Ration Transformation DT: Decision Tree IG: Information Gain SVM: Support Vector Machine FAR: False Alram Rate DR: Detection Rate ROC: Re
proposed SAPAS for identifying pAs from poly(A)-containing reads and quantifying pAs in peak regions determined by a parametric clustering algorithm [102]. They further applied SAPAS to the scRNA-seq data of GABAergic neurons and detected cell type-specific APA events and cell-to-cell modality...
instead, addresses the challenges of low density and imbalanced distribution of sparse depth cues by expanding the depth estimates around sparse signals based on the RGB image and controlling their influence through confidence weighting.LSMD-NetYin et al. (2022) fuses LiDAR and stereo information usi...
Big data has a substantial role nowadays, and its importance has significantly increased over the last decade. Big data’s biggest advantages are providing knowledge, supporting the decision-making process, and improving the use of resources, services, a
SemSense [23] uses DBSCAN density clustering algorithm [76] based on WiFi, location and images to cluster room landmarks. 5.2. Trajectory-based association and alignment The landmark-based association is efficient but is vulnerable to noise. Therefore, some methods propose to use trajectory-based...
22 Highway Evasion Detection and Analysis Based on Improved Fast Peak Clustering Algorithm ... 23 Detection of Traffic Congestion from Ultra-Low Frame Rate Images Applied in Large Regional Surveillance System via Deep Residual TrafficNet...
A Fingerprint Localization Algorithm Based on Low-Density Tags Indoor location, Fingerprint location Document Understanding with Multi-modal Large Language Model : A Survey Document Understanding, Multi-modal Large Language, Document AI, Document VQA Towards human-like multimodal perception and cognition: ...
3 METHODOLOGIES In this survey, given that the current mainstream approaches continue to incorporate some traditional algorithmic concepts, which aim to generate density maps and implement regres- sion models, we find it essential to examine earlier conventional methods. We focus on the evolution of ...
clustering (SC)’s limitations, various extensions based on power iteration have been developed, such as Arnoldi iteration [132], the Lanczos method [133], Implicitly Restarted Lanczos Method [134] and Krylov-Schur algorithm [135].(2)Sampling approach: Sampling approaches [136] work on a ...
[159] considers using density clustering on incomplete datasets. It proposes a novel two-stage missing feature density peak clustering method. Firstly, density peak clustering algorithm is applied to data with complete features, while using labeled core points representing the entire data distribution ...