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Semisupervised Clustering by Queries and Locally Encodable Source Coding
[article]
2020
arXiv
pre-print
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In this paper, we show that a recently popular model of semi-supervised clustering is equivalent to locally encodable source coding. ...
Source coding is the canonical problem of data compression in information theory. In a locally encodable source coding, each compressed bit depends on only few bits of the input. ...
Semisupervised Clustering by Queries and Locally Encodable Source Coding
Arya Mazumdar Senior Member, IEEE, Soumyabrata Pal Abstract-Source coding is the canonical problem of data compression in information ...
arXiv:1904.00507v2
fatcat:ngogbnouenfvtk5i5d3572bxj4
Learning to Hash for Indexing Big Data—A Survey
2016
Proceedings of the IEEE
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We provide a comprehensive survey of the learning-to-hash framework and representative techniques of various types, including unsupervised, semisupervised, and supervised. ...
In many critical applications such as largescale search and pattern matching, finding the nearest neighbors to a query is a fundamental research problem. ...
One way to find nearest neighbors of the query is by computing the Hamming distance between the query code to all the database codes. ...
doi:10.1109/jproc.2015.2487976
fatcat:4eok2ubzxnc5nmc4hgt4qmqhcy
IEEE Access Special Section Editorial: Advanced Data Mining Methods for Social Computing
2020
IEEE Access
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YONGQIANG ZHAO (Member, IEEE) received the B.S. degree in automation and the M.S. and Ph.D. degrees in control theory and control engineering from Northwestern ...
an efficient incremental semisupervised classification method named CODES (Classification Over Drifting and Evolving Stream). ...
The article by Bi et al., ''CODES: Efficient incremental semisupervised classification over drifting and evolving social streams,'' addresses the major challenges of social stream classification by proposing ...
doi:10.1109/access.2020.3043060
fatcat:qbqk5f4ojvadlazhk2mc343sra
Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification
[article]
2019
arXiv
pre-print
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Without spending much effort on labeling, our SSG ++ can further promote the mAP upon SSG by 10.7% and 6.9%, respectively. Our Code is available at: https://github.com/OasisYang/SSG . ...
Concretely, we propose a Self-similarity Grouping (SSG) approach, which exploits the potential similarity (from global body to local parts) of unlabeled samples to automatically build multiple clusters ...
Acknowledgements: This work is part supported by IBM-ILLINOIS Center for Cognitive Computing Systems Research (C3SR) and ARC DECRA DE190101315. ...
arXiv:1811.10144v3
fatcat:wjvajgjvsjctjpo45nmouqiifa
Hashing Techniques
2017
ACM Computing Surveys
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Hashing techniques have also evolved from simple randomization approaches to advanced adaptive methods considering locality, structure, label information, and data security, for effective hashing. ...
With the rapid development of information storage and networking technologies, quintillion bytes of data are generated every day from social networks, business transactions, sensors, and many other domains ...
ACKNOWLEDGMENTS This research is partially sponsored by the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning. ...
doi:10.1145/3047307
fatcat:u5asusjs7vdq7f3a6wgnesnodq
Supporting AI Engineering on the IoT Edge through Model-Driven TinyML
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2107.02690v1 fatcat:kw2h5cclcjb5zcqius5mw2qlue
Software engineering of network-centric Artificial Intelligence (AI) and Internet of Things (IoT) enabled Cyber-Physical Systems (CPS) and services, involves complex design and validation challenges. ...
We focus on a sub-discipline of AI, namely Machine Learning (ML) and propose the delegation of data analytics and ML to the IoT edge. ...
The authors would like to also thank Stephan Rössler from Software AG and Marouane Sayih (alumnus of the Technical University of Munich) for their collaboration and support. ...
arXiv:2107.02690v2
fatcat:3yuy6b67cne4zmohlmh6rmsxdm
Contrastive Representation Learning: A Framework and Review
[article]
2020
arXiv
pre-print
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However, the origins of Contrastive Learning date as far back as the 1990s and its development has spanned across many fields and domains including Metric Learning and natural language processing. ...
In this paper we provide a comprehensive literature review and we propose a general Contrastive Representation Learning framework that simplifies and unifies many different contrastive learning methods ...
The color for defining similarity in query and keys encodes: Multi-sensory, Data transformation, Context-Instance, Sequential Coherence, Clustering. ...
arXiv:2010.05113v1
fatcat:xdegcaoarvevdfl4r22pyzqr4e
Contrastive Representation Learning: A Framework and Review
2020
IEEE Access
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Science Foundation Ireland through the SFI Centre for Research Training in Machine Learning (18/CRT/6183) and the Insight Centre for Data Analytics (SFI/12/RC/2289_P2). ...
The color for defining similarity in query and keys encodes: Multi-sensory, Data transformation, Context-Instance, Sequential Coherence, Clustering. ...
The color for defining similarity in query and keys encodes: Multi-sensory, Data transformation, Context-Instance, Sequential Coherence, Clustering. ...
doi:10.1109/access.2020.3031549
fatcat:qohhn2f2tray5ha3iafxbnwp74
Deep Hashing for Semi-supervised Content Based Image Retrieval
2018
KSII Transactions on Internet and Information Systems
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Proposed approach uses semi-supervised deep hashing with semantic learning and binary code generation by minimizing the objective function. ...
Proposed activation and loss functions helped to minimize classification error and produce better hash codes. ...
Our goal is to learn a mapping function Z: X {0,1}Q that can encodes any image x ϵ X into Q-bit binary code by preserving semantic similarity. Convolutional autoencoder was used by Ranzato et. al. ...
doi:10.3837/tiis.2018.08.013
fatcat:lessznvvsva65ip743jbfu425q
Locality-Sensitive Hashing Techniques for Nearest Neighbor Search
2012
International Journal of Fuzzy Logic and Intelligent Systems
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This paper introduces the notion of locality-sensitive hashing and surveys the locality-sensitive hashing techniques. ...
Nearest neighbor search is such a task which finds from a data set the k nearest data points to queries. ...
The hash functions by Indyk et al. [16] are defined as follows: First, data points are encoded into binary codes in the Hamming space. ...
doi:10.5391/ijfis.2012.12.4.300
fatcat:qwkw27hpd5ht5jhzwfgcajyfs4
Vehicle Re-Identification Based on Complementary Features
[article]
2020
arXiv
pre-print
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The codes are available at https://github.com/gggcy/AIC2020_ReID. ...
Due to the vehicle's orientation, lighting and inter-class similarity, it is difficult to achieve robust and discriminative representation feature. ...
A popular re-ranking approach is k-reciprocal encoding [26] . By encoding k-reciprocal nearest neighbors into a single vector. ...
arXiv:2005.04463v1
fatcat:bunl4ccv2zacvhymnmpbn2i2mi
Applications of Multi-view Learning Approaches for Software Comprehension
2019
The Art, Science, and Engineering of Programming
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2019 pre-print arXiv:1902.00526v1 fatcat:la2wtczsafa7rjzq5sjusdfphq
high-level units and give component-level recommendations for refactoring of the system, as well as cross-view source code search. ...
A software system consists of various views including the module dependency graph, execution logs, evolutionary information and the vocabulary used in the source code, that collectively defines the software ...
The similarity between a query and a source code unit is defined based on their textual feature vectors. ...
doi:10.22152/programming-journal.org/2019/3/14
fatcat:5bw467krprefzdhw7nvu4ib3wm
Application of Convolutional Neural Networks for Stellar Spectral Classification
2019
Monthly notices of the Royal Astronomical Society
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to solve spectral classification and regression problems like the determination of stellar atmospheric parameters Teff, $\rm {\log g}$, and [Fe/H]. ...
, ELODIE and MILES spectral libraries as training samples. ...
AC K N OW L E D G E M E N T S AK and KS acknowledge financial support from a Raja Ramanna Fellowship (10/1( 16 )/2016/RRF-R&D-II/630) awarded by Department of Atomic Energy, Government of India. ...
doi:10.1093/mnras/stz3100
fatcat:7cgunurcffdqtm34nxvzer3f6y
A Survey on Deep Hashing Methods
2022
ACM Transactions on Knowledge Discovery from Data
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Nearest neighbor search aims to obtain the samples in the database with the smallest distances from them to the queries, which is a basic task in a range of fields, including computer vision and data mining ...
, we categorize deep supervised hashing methods into pairwise methods, ranking-based methods, pointwise methods as well as quantization according to how measuring the similarities of the learned hash codes ...
ACKNOWLEDGMENTS This work was supported by the National Key Research and Development Program of China (2021YFF1200902) and the National Natural Science Foundation of China (31871342). ...
doi:10.1145/3532624
fatcat:7lxtu2qzvvhrpnjngefli2mvca
A Survey on Visual Content-Based Video Indexing and Retrieval
2011
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
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retrieval including query interfaces, similarity measure and relevance feedback, and video browsing. ...
This paper offers a tutorial and an overview of the landscape of general strategies in visual content-based video indexing and retrieval, focusing on methods for video structure analysis, including shot ...
Query Types Nonsemantic-based video query types include query by example, query by sketch, and query by objects. ...
doi:10.1109/tsmcc.2011.2109710
fatcat:qtenus4htffcfbyuiwidgjojku
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