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A New Graph Constructor for Semi-supervised Discriminant Analysis via Group Sparsity

Haoyuan Gao, Liansheng Zhuang, Nenghai Yu
2011 2011 Sixth International Conference on Image and Graphics  
Inspired by the advances of compressive sensing, we propose a novel graph construction method via group sparsity,which means to constrain the reconstruct data to be sparse for each sample, and constrain  ...  This paper studies the Semi-supervised Discriminant Analysis (SDA) algorithm, which aims at dimensionality reduction utilizing both limited labeled data and abundant unlabeled data.  ...  ACKNOWLEDGMENT We would like to thank anonymous reviewers for their comments.  ... 
doi:10.1109/icig.2011.82 dblp:conf/icig/GaoZY11 fatcat:ju6j36znsnggzm7kv2iguyxjlu

Detection of Malicious Software by Analyzing Distinct Artifacts Using Machine Learning and Deep Learning Algorithms

Mathew Ashik, A. Jyothish, S. Anandaram, P. Vinod, Francesco Mercaldo, Fabio Martinelli, Antonella Santone
2021 Electronics  
Malware analysis and prevention methods are increasingly becoming necessary for computer systems connected to the Internet.  ...  Prominent features are extracted using Minimum Redundancy and Maximum Relevance (mRMR) and Analysis of Variance (ANOVA).  ...  The training process can be either supervised, unsupervised, and semi-supervised. Supervised feature selection determines feature relevance by evaluating the correlation of attributes with the class.  ... 
doi:10.3390/electronics10141694 fatcat:wj5oa566gzcjldfq62kqnc3mrm

Extracting event and their relations from texts: A survey on recent research progress and challenges

Kang Liu, Yubo Chen, Jian Liu, Xinyu Zuo, Jun Zhao
2020 AI Open  
A B S T R A C T Event is a common but non-negligible knowledge type.  ...  This paper summaries some constructed event-centric knowledge graphs and the recent typical approaches for event and event relation extraction, besides task description, widely used evaluation datasets  ...  curated semi-structured sources.  ... 
doi:10.1016/j.aiopen.2021.02.004 fatcat:qxbcmk55vzcb5nznhgfgwrbe4u

Learning with Kernels and Logical Representations [chapter]

Paolo Frasconi, Andrea Passerini
2008 Lecture Notes in Computer Science  
The relational representation of data and background knowledge are used to form a kernel function, enabling us to subsequently apply a number of kernel-based statistical learning algorithms.  ...  In this chapter, we describe a view of statistical learning in the inductive logic programming setting based on kernel methods.  ...  A special thanks goes to Alessio Ceroni, Fabrizio Costa, Kristian Kersting, Sauro Menchetti, and Jan Ramon, who helped us in numerous occasions with fruitful discussions.  ... 
doi:10.1007/978-3-540-78652-8_3 fatcat:tifoqknicffuxe5kgaup6p3tc4

Opportunities and obstacles for deep learning in biology and medicine

Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin, Brian T. Do, Gregory P. Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M. Hoffman, Wei Xie, Gail L. Rosen (+24 others)
2018 Journal of the Royal Society Interface  
Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records.  ...  Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features.  ...  We thank Aaron Sheldon, who contributed text but did not formally approve the manuscript; Anna Greene for a careful proofreading of the manuscript in advance of the first submission; Sebastian Raschka  ... 
doi:10.1098/rsif.2017.0387 pmid:29618526 pmcid:PMC5938574 fatcat:65o4xmp53nc6zmj37srzuht6tq

Mining the Semantic Web

Achim Rettinger, Uta Lösch, Volker Tresp, Claudia d'Amato, Nicola Fanizzi
2012 Data mining and knowledge discovery  
This is intended to show the breadth and general potential of this exiting new research and application area for data mining.  ...  From another perspective, the more expressive structured representations open up new opportunities for data mining, knowledge extraction and machine learning techniques.  ...  For the area of social network analysis this could mean finding similar persons to identify sub-groups of people.  ... 
doi:10.1007/s10618-012-0253-2 fatcat:qhkv27bbkvba3mlolhixrepioq

Graph Neural Networks for Natural Language Processing: A Survey [article]

Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, Bo Long
2021 arXiv   pre-print
As a result, thereis a surge of interests in developing new deep learning techniques on graphs for a large numberof NLP tasks.  ...  We propose a new taxonomy of GNNs for NLP, whichsystematically organizes existing research of GNNs for NLP along three axes: graph construction,graph representation learning, and graph based encoder-decoder  ...  Semi-supervised entity alignment via joint knowledge embedding model and cross-graph model.  ... 
arXiv:2106.06090v1 fatcat:zvkhinpcvzbmje4kjpwjs355qu

Semantic Similarity from Natural Language and Ontology Analysis

Sébastien Harispe, Sylvie Ranwez, Stefan Janaqi, Jacky Montmain
2015 Synthesis Lectures on Human Language Technologies  
The two state-of-the-art approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based  ...  Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains towards a better understanding of semantic similarity  ...  These measures rely on algorithms designed for graph analysis which are generally used in a straightforward manner.  ... 
doi:10.2200/s00639ed1v01y201504hlt027 fatcat:y3tbtmwwqbhydeuaf2dlqk62ui

Multi-word Entity Classification in a Highly Multilingual Environment

Sophie Chesney, Guillaume Jacquet, Ralf Steinberger, Jakub Piskorski
2017 Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)  
The program also included a panel discussion on the future directions of the MWE community and the SIGLEX Section.  ...  We would like to thank the members of the program committee for the timely reviews, authors for their valuable contributions, shared task organizers, annotators, and system developers for their hard work  ...  Juan de la Cierva formación grant (FJCI-2014-22853), and by a postdoctoral fellowship granted by the Galician Government (POS-A/2013/191).  ... 
doi:10.18653/v1/w17-1702 dblp:conf/mwe/ChesneyJSP17 fatcat:bv7aavgth5eurmzuphuowtuuhq

Graph mining on static, multiplex and attributed networks [article]

Benedek Rózemberczki, University Of Edinburgh, Rik Sarkar, He Sun
Relational data poses challenges for information extraction and knowledge discovery due to its web scale size, extreme sparsity, multimodality, the presence of spatial autocorrelation and heterogeneity  ...  Graph structured data is pervasive and generated by online human interactions at an unprece- dented velocity.  ...  network with a downstream semi-supervised model.  ... 
doi:10.7488/era/1498 fatcat:kmqs4lnzmzam7a7q53aic7nf3y

Learning Capacity in Simulated Virtual Neurological Procedures

Mattia Samuel Mancosu, Silvester Czanner, Martin Punter
2020 Journal of WSCG  
ACKNOWLEDGEMENTS The authors would like to thank Oana Rotaru-Orhei for her comments and the three anonymous reviewers for their insightful suggestions.  ...  This work was partially supported by a grant of Ministry of Research and Innovation, CNCS -UEFISCDI, project number PN-III-P4-ID-PCE-2016-0842, within PNCDI III.  ...  The Linear Discriminant Analysis is applied as a supervised data reduction technique.  ... 
doi:10.24132/csrn.2020.3001.13 fatcat:uytlm7nytrhmnk553ellfhl54a

Data Mining Algorithms for Decentralized Fault Detection and Diagnostic in Industrial Systems [article]

(:Unkn) Unknown, University, My, Slobodan Vucetic
For small-scale sensor networks, it is reasonable to assume that all measurements are available at a central location (sink) where fault predictions are made.  ...  Once a fault has been detected pinpointing the type of fault is needed for purposes of fault mitigation and returning to normal process operation. This is known as Fault Diagnosis.  ...  The semi-supervised model f ss is retrained upon receipt of a new batch of labeled data by solving (4.8).  ... 
doi:10.34944/dspace/1320 fatcat:in4zukomcbfmlkt3xgbshs6n3m

Learning with Kernels and Logical Representations [chapter]

Paolo Frasconi
Inductive Logic Programming  
The relational representation of data and background knowledge are used to form a kernel function, enabling us to subsequently apply a number of kernel-based statistical learning algorithms.  ...  In this chapter, we describe a view of statistical learning in the inductive logic programming setting based on kernel methods.  ...  A special thanks goes to Alessio Ceroni, Fabrizio Costa, Kristian Kersting, Sauro Menchetti, and Jan Ramon, who helped us in numerous occasions with fruitful discussions.  ... 
doi:10.1007/978-3-540-78469-2_1 dblp:conf/ilp/Frasconi07 fatcat:mb775tlbqzeg7owzjrhnoq76wm

Distance and kernel based learning over composite representations

Adam Woznica, Christian Pellegrini, Mélanie Hilario, Alexandros Kalousis
Un remerciement spécial va à tous les autres membres du Groupe d'Intelligence Artificielle grâce auxquels j'ai pu évolué dans un environnement stimulant, en particulier pour toutes les (longues) discussions  ...  Alexandros Kalousis pour m'avoir accepté comme doctorant au sein du Groupe d'Intelligence Artificielle de l'Université de Genève.  ...  Remerciements J'ai maintenant le plaisir et la joie de saisir cette occasion pour remercier toutes les personnes qui m'ont accompagné et m'ont soutenu au cours des années de travail qui ont conduit à cette  ... 
doi:10.13097/archive-ouverte/unige:5483 fatcat:fkmt5sko2bek5mmucwiegg45tq

Multi-Modal Technology for User Interface Analysis including Mental State Detection and Eye Tracking Analysis

Ahmed Husseini Orabi, Université D'Ottawa / University Of Ottawa, Université D'Ottawa / University Of Ottawa
For video data retrieval, we use a basic webcam. We investigate combinations of observation modalities to detect and extract affective and mental states.  ...  We report on the recognition accuracy of basic emotions for each modality.  ...  Semi-supervised quantitative evaluation We test the performance of semi-supervised learning using DTCN. The evaluation is based on a standard digit classification benchmark, MNIST.  ... 
doi:10.20381/ruor-20731 fatcat:5ayqafaobzeavgxrcznn7n42q4
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