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 Qiang Cheng

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Average citations per article8.39
Citation Count151
Publication count18
Publication years2000-2017
Available for download6
Average downloads per article445.67
Downloads (cumulative)2,674
Downloads (12 Months)456
Downloads (6 Weeks)57
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19 results found Export Results: bibtexendnoteacmrefcsv

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1 published by ACM
June 2018 ACM Transactions on Intelligent Systems and Technology (TIST) - Research Survey and Regular Papers: Volume 9 Issue 5, July 2018
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 30,   Downloads (12 Months): 74,   Downloads (Overall): 74

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In this article, we introduce a novel, general methodology, called integrate and conquer, for simultaneously accomplishing the tasks of feature extraction, manifold construction, and clustering, which is taken to be superior to building a clustering method as a single task. When the proposed novel methodology is used on two-dimensional (2D) ...
Keywords: Clustering, feature extraction, two-dimensional data, unsupervised learning

2
December 2017 Neurocomputing: Volume 267 Issue C, December 2017
Publisher: Elsevier Science Publishers B. V.
Bibliometrics:
Citation Count: 1

Similarity measure is fundamental to many machine learning and data mining algorithms. Predefined similarity metrics are often data-dependent and sensitive to noise. Recently, data-driven approach which learns similarity information from data has drawn significant attention. The idea is to represent a data point by a linear combination of all (other) ...
Keywords: Clustering, Sparse representation, Kernel method, Nonlinear relation, Recommender systems, Multiple kernel learning, Similarity measure

3 published by ACM
March 2017 ACM Transactions on Knowledge Discovery from Data (TKDD): Volume 11 Issue 3, April 2017
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 14,   Downloads (12 Months): 233,   Downloads (Overall): 495

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Matrix factorization is often used for data representation in many data mining and machine-learning problems. In particular, for a dataset without any negative entries, nonnegative matrix factorization (NMF) is often used to find a low-rank approximation by the product of two nonnegative matrices. With reduced dimensions, these matrices can be ...
Keywords: Nonnegative factorization, clustering, manifold, robust principal component analysis

4 published by ACM
January 2017 ACM Transactions on Intelligent Systems and Technology (TIST) - Special Issue: Mobile Social Multimedia Analytics in the Big Data Era and Regular Papers: Volume 8 Issue 3, April 2017
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 7,   Downloads (12 Months): 137,   Downloads (Overall): 290

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Matrix factorization is a useful technique for data representation in many data mining and machine learning tasks. Particularly, for data sets with all nonnegative entries, matrix factorization often requires that factor matrices be nonnegative, leading to nonnegative matrix factorization (NMF). One important application of NMF is for clustering with reduced ...
Keywords: Non-negative matrix factorization, clustering, feature learning, manifold learning

5
December 2011 IEEE Transactions on Neural Networks - Part 1: Volume 22 Issue 12, December 2011
Publisher: IEEE Press
Bibliometrics:
Citation Count: 1

This brief presents a dynamical system approach to vector quantization or clustering based on ordinary differential equations with the potential for real-time implementation. Two examples of different pattern clusters demonstrate that the model can successfully quantize different types of input patterns. Furthermore, we analyze and study the stability of our ...

6
June 2011 IEEE Transactions on Pattern Analysis and Machine Intelligence: Volume 33 Issue 6, June 2011
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 15

Selecting features for multiclass classification is a critically important task for pattern recognition and machine learning applications. Especially challenging is selecting an optimal subset of features from high-dimensional data, which typically have many more variables than observations and contain significant noise, missing components, or outliers. Existing methods either cannot handle ...
Keywords: Classification, feature subset selection, Fisher's linear discriminant analysis, high-dimensional data, kernel, Markov random field.

7
October 2010 IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB): Volume 7 Issue 4, October 2010
Publisher: IEEE Computer Society Press
Bibliometrics:
Citation Count: 3
Downloads (6 Weeks): 0,   Downloads (12 Months): 5,   Downloads (Overall): 84

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Extracting features from high-dimensional data is a critically important task for pattern recognition and machine learning applications. High-dimensional data typically have much more variables than observations, and contain significant noise, missing components, or outliers. Features extracted from high-dimensional data need to be discriminative, sparse, and can capture essential characteristics of ...
Keywords: High-dimensional data, feature selection, persistence, bias, convex optimization, primal-dual interior-point optimization, cancer classification, microarray gene analysis.

8 published by ACM
March 2010 SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 1,   Downloads (12 Months): 1,   Downloads (Overall): 68

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A class of Maximum A Posteriori(MAP) formulations built on various graph models are of great interests for both theoretical and practical applications. Recent advances in this field have extended the connections between the linear program (LP) relaxation and various tree-reweighted message passing algorithms. At both sides, many algorithms and their ...
Keywords: MAP optimality, Markov random field, linear program

9
August 2009 CSE '09: Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 02
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 2

Accuracy and security of medical information, when a patient vital sign signal such as respiratory sounds must be transmitted through a communication channel, are two essential important features in design of telemedicine systems and in medical diagnosis. This paper studies medical signal accuracy in WiFi-based telemedicine system. When system resources ...
Keywords: telehealth, accuracy, security, medical pattern recognition

10
July 2009 IEEE Transactions on Circuits and Systems for Video Technology: Volume 19 Issue 7, July 2009
Publisher: IEEE Press
Bibliometrics:
Citation Count: 6

This paper constructs a class of generalized embeddings of multiplicative watermarks. Ordinary multiplicative and additive methods are included as special cases. The new watermarks automatically adapt to the local contents of host signals, benefiting the perceptual quality. The decoding makes use of the optimal generalized correlation detector. The host interference ...
Keywords: Data hiding and watermarking, data hiding and watermarking, image modeling, generalized embedding, generalized correlator, performance analysis

11
February 2008 IEEE Transactions on Computers: Volume 57 Issue 2, February 2008
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 20

In this work, we consider the sensor localization problem from a novel perspective by treating it as a functional dual of target tracking. In traditional tracking problems, static location-aware sensors track and predict the position/speed of a moving target. As a dual, we utilize a moving location-assistant (LA) (with global ...
Keywords: Algorithm/protocol design and analysis, Sensor networks, Sensor networks, Algorithm/protocol design and analysis, Wireless, Wireless

12
August 2004 ICPR '04: Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 0

This paper considers visualizing and summarizing image sequences using manifold learning and multiresolution techniques. The images in a video are found usually lying on a significantly low-dimensional manifold, which provides intrinsic information on the video content and formation. The parametrization of the manifold is discovered using a nonlinear subspace method ...

13
August 2004 Signal Processing: Volume 84 Issue 8, August 2004
Publisher: Elsevier North-Holland, Inc.
Bibliometrics:
Citation Count: 0

Digital watermarking is an important technique to protect intellectual property right and to transmit useful secondary data. This paper investigates the performance analysis and error exponents of asymmetric watermarking systems. Asymmetric watermarking provides potentially better levels of security, its detection is much different from commonly used watermarking detectors, particularly when ...
Keywords: asymmetric watermarking, exponents characteristic curve, locally optimum detector, exponential level, exponential power, asymptotic performance analysis

14
April 2003 IEEE Transactions on Signal Processing: Volume 51 Issue 4, April 2003
Publisher: IEEE Press
Bibliometrics:
Citation Count: 39

Digital watermarking is an emerging technique to protect data security and intellectual property right. Identification or verification of watermarking patterns can be achieved by detecting watermarks in received signals. However, one of the biggest challenges in watermarking detection is that the strengths of the watermark signals will change after being ...

15
January 2002
Bibliometrics:
Citation Count: 0


16 published by ACM
October 2001 MULTIMEDIA '01: Proceedings of the ninth ACM international conference on Multimedia
Publisher: ACM
Bibliometrics:
Citation Count: 5
Downloads (6 Weeks): 4,   Downloads (12 Months): 6,   Downloads (Overall): 1,118

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An image watermarking technique based on pyramid transforms is proposed. An arbitrary binary pattern is formed into an effective hypothesized pattern and transmitted as a watermark. Multiresolution pyramid transforms are applied to host images, whose characteristics are exploited to embed the watermark. The detector is designed to be effective to ...
Keywords: pyramid transfrom, verification coding, watermarking

17
September 2001 IEEE Transactions on Multimedia: Volume 3 Issue 3, September 2001
Publisher: IEEE Press
Bibliometrics:
Citation Count: 45

This paper presents an additive approach to transform-domain information hiding and the performance analysis for images and video. The watermark embedding method is designed to satisfy the perceptual constraints and improve the detectability as well as the information embedding rate. The statistical behaviors of subband coefficients are modeled by the ...

18
May 2001 ICASSP '01: Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 11

The technique of embedding a digital signal into an audio recording or image using techniques that render the signal imperceptible has received significant attention. Embedding an imperceptible, cryptographically secure signal, or watermark, is seen as a potential mechanism that may be used to prove ownership or detect tampering. While there ...

19 published by ACM
October 2000 MULTIMEDIA '00: Proceedings of the eighth ACM international conference on Multimedia
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 1,   Downloads (12 Months): 2,   Downloads (Overall): 545

Full text available: PDFPDF
The temporal redundancy of video provides a greater space than images for information hiding at the expense of invitation towards many forms of spatial and temporal attacks, such as frame dropping, frame averaging that are not common in images. With video, the active change of watermark placement location serves as ...
Keywords: video watermarking, Principal Component Analysis, Region of Interest, clustering, PCA



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