Semi-Supervised Clustering with Neural Networks

Autor:	Ankita Shukla, Saket Anand, Gullal Singh Cheema
Jazyk:	angličtina
Rok vydání:	2018
Předmět:	FOS: Computer and information sciences Computer Science::Machine Learning Computer Science - Machine Learning Kullback–Leibler divergence Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Machine Learning (stat.ML) 02 engineering and technology Semi-supervised learning 010501 environmental sciences 01 natural sciences Machine Learning (cs.LG) Semantic similarity Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering Cluster analysis 0105 earth and related environmental sciences Artificial neural network business.industry 020207 software engineering Pattern recognition Autoencoder ComputingMethodologies_PATTERNRECOGNITION Unsupervised learning Pairwise comparison Artificial intelligence business
Zdroj:	BigMM
Popis:	Clustering using neural networks has recently demonstrated promising performance in machine learning and computer vision applications. However, the performance of current approaches is limited either by unsupervised learning or their dependence on large set of labeled data samples. In this paper, we propose ClusterNet that uses pairwise semantic constraints from very few labeled data samples ( 9 Pages
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2542356b0ed28f17d235afa7cf160c0b http://arxiv.org/abs/1806.01547 Zobrazit plný text záznamu