Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics

• Other Authors: Gou, Jianping (Editor), Ou, Weihua (Editor), Zeng, Shaoning (Editor), Du, Lan (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: Information technology industries; Computer science; head detection; YoloV4; NMS; soft-NMS; people counting; vehicle re-identification; license plate recognition; video surveillance; feature extraction; pedestrian detection; machine learning; end-to-end; anchor-free; feature reuse; correlation filters; second-order fitting; visual tracking; DCNN-BiLSTM; domain adaptation; MMD; fine-tuning; C-MAPSS; cross-working; small sample; blind image deblurring; image prior; sparse channel; sparsity; multi-output; kNN; metric learning; cost-weighted; geometric mean metric; motion deblurring; image super-resolution; multi-order attention; gated learning; decoupling; face recognition; second-order gradient; image gradient orientations; collaborative-representation-based classification; image aesthetic assessment; semi-supervised learning; label propagation; deep learning; computer vision; garbage quantity identification; YOLOX; Soft-NMS; stability; switched system; state-dependent switching; time delay; multi-source domain adaptation; Dempster-Shafer evidence theory; cross-domain classification; 3D reconstruction; multi-view stereo; structure from motion; background matting; adversarial example; feature transformation; black-box attack; ensemble attack; deep neural network; intelligent design; data analysis; models and algorithms; extension theory; scheme design; adversarial learning; adversarial equilibrium; transferability quantification; power load forecasting; routing, modulation and spectrum assignment; elastic optical networks; deep reinforcement learning; knowledge distillation; aspect-based sentiment analysis; graph neural networks; dependency trees; dependency types; graph attention mechanism; syntactic; semantic; vehicle color recognition; low-high level joint task; object detection; joint semantic learning; rainy image recovery; XSS attack; traffic detection; payloads; fusion verification; hypergraph matching; similarity metric; information-theoretic metric learning; mixed noise removal; matrix nuclear norm; logarithm norm; ADMM; plug-and-play; aspect-level sentiment classification; external knowledge; KGE; GCN; discriminative feature learning; multidimensional scaling; fuzzy k-means; pairwise constraint propagation; iterative majorization algorithm; Aspect Level Sentiment Classification; Contrasitve Learning; Graph Convolutional Networks; graph convolutional networks; commonsense knowledge graph; anomaly detection; cyber-physical; industrial control systems; image classification; large-margin technique; robustness; anti-noise performance; cross-domain sentiment classification; word embedding; GAT; hate speech detection; contrastive learning; multi-task learning; attention mechanism; state reconstruction; gait adjustment; uncertain temporal knowledge graph; temporal knowledge graph; knowledge graph embedding; confidence score; n/a
• Online Access: DOAB: download the publication; DOAB: description of the publication

Summary

The present reprint contains 33 articles accepted and published in the Special Issue entitled "Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics, 2022" in the MDPI journal, Mathematics, which covers a wide range of topics connected to the theory and applications of feature representation learning for image processing, artificial intelligence, data mining and robotics. These topics include, among others, elements from image blurring, image aesthetic quality assessment, pedestrian detection, visual tracking, vehicle re-identification, face recognition, 3D reconstruction, the stability of switched systems, domain adaption, deep reinforcement, sentiment analysis, graph convolutional networks, knowledge graphs, geometric metric learning, etc. It is hoped that this reprint will be interesting and useful for those working in the area of image processing, computer vision, machine learning, natural language processing and robotics, as well as for those with backgrounds in machine learning who are willing to become familiar with recent advancements in artificial intelligence, which, today, is present in almost all aspects of human life and activities.

Physical Description

1 electronic resource (556 p.)

ISBN

books978-3-0365-7263-5
9783036572628
9783036572635

Access

Open Access