IEEE Organizations related to Dimensionality Reduction

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Conferences related to Dimensionality Reduction

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2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE International Conference on Multimedia and Expo (ICME)

Multimedia technologies, systems and applications for both research and development of communications, circuits and systems, computer, and signal processing communities.

  • 2019 IEEE International Conference on Multimedia and Expo (ICME)

    speech, audio, image, video, text and new sensor signal processingsignal processing for media integration3D imaging, visualization and animationvirtual reality and augmented realitymulti-modal multimedia computing systems and human-machine interactionmultimedia communications and networkingmedia content analysis and searchmultimedia quality assessmentmultimedia security and content protectionmultimedia applications and servicesmultimedia standards and related issues

  • 2018 IEEE International Conference on Multimedia and Expo (ICME)

    The IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE societies since 2000. It serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities. ICME also features an Exposition of multimedia products and prototypes.

  • 2017 IEEE International Conference on Multimedia and Expo (ICME)

    Topics of interest include, but are not limited to: – Speech, audio, image, video, text and new sensor signal processing – Signal processing for media integration – 3D visualization and animation – 3D imaging and 3DTV – Virtual reality and augmented reality – Multi-modal multimedia computing systems and human-machine interaction – Multimedia communications and networking – Media content analysis – Multimedia quality assessment – Multimedia security and content protection – Multimedia databases and digital libraries – Multimedia applications and services – Multimedia standards and related issues

  • 2016 IEEE International Conference on Multimedia and Expo (ICME)

    Topics of interest include, but are not limited to:- Speech, audio, image, video, text and new sensor signal processing- Signal processing for media integration- 3D visualization and animation- 3D imaging and 3DTV- Virtual reality and augmented reality- Multi-modal multimedia computing systems and human-machine interaction- Multimedia communications and networking- Media content analysis- Multimedia quality assessment- Multimedia security and content protection- Multimedia databases and digital libraries- Multimedia applications and services- Multimedia standards and related issues

  • 2015 IEEE International Conference on Multimedia and Expo (ICME)

    With around 1000 submissions and 500 participants each year, the IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE societies since 2000. It serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2014 IEEE International Conference on Multimedia and Expo (ICME)

    The IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE societies since 2000. It serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications. In 2014, an Exposition of multimedia products, prototypes and animations will be held in conjunction with the conference.Topics of interest include, but are not limited to:

  • 2013 IEEE International Conference on Multimedia and Expo (ICME)

    To promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2012 IEEE International Conference on Multimedia and Expo (ICME)

    IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE Societies. It exchanges the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2011 IEEE International Conference on Multimedia and Expo (ICME)

    Speech, audio, image, video, text processing Signal processing for media integration 3D visualization, animation and virtual reality Multi-modal multimedia computing systems and human-machine interaction Multimedia communications and networking Multimedia security and privacy Multimedia databases and digital libraries Multimedia applications and services Media content analysis and search Hardware and software for multimedia systems Multimedia standards and related issues Multimedia qu

  • 2010 IEEE International Conference on Multimedia and Expo (ICME)

    A flagship multimedia conference sponsored by four IEEE societies, ICME serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2009 IEEE International Conference on Multimedia and Expo (ICME)

    IEEE International Conference on Multimedia & Expo is a major annual international conference with the objective of bringing together researchers, developers, and practitioners from academia and industry working in all areas of multimedia. ICME serves as a forum for the dissemination of state-of-the-art research, development, and implementations of multimedia systems, technologies and applications.

  • 2008 IEEE International Conference on Multimedia and Expo (ICME)

    IEEE International Conference on Multimedia & Expo is a major annual international conference with the objective of bringing together researchers, developers, and practitioners from academia and industry working in all areas of multimedia. ICME serves as a forum for the dissemination of state-of-the-art research, development, and implementations of multimedia systems, technologies and applications.

  • 2007 IEEE International Conference on Multimedia and Expo (ICME)

  • 2006 IEEE International Conference on Multimedia and Expo (ICME)

  • 2005 IEEE International Conference on Multimedia and Expo (ICME)

  • 2004 IEEE International Conference on Multimedia and Expo (ICME)

  • 2003 IEEE International Conference on Multimedia and Expo (ICME)

  • 2002 IEEE International Conference on Multimedia and Expo (ICME)

  • 2001 IEEE International Conference on Multimedia and Expo (ICME)

  • 2000 IEEE International Conference on Multimedia and Expo (ICME)


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

All fields of satellite, airborne and ground remote sensing.


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Periodicals related to Dimensionality Reduction

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Most published Xplore authors for Dimensionality Reduction

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Xplore Articles related to Dimensionality Reduction

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An Analysis and Research of Type-2 Diabetes TCM Records Based On Text Mining

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018

This paper analyzes 152 type-2 diabetes Traditional Chinese Medicine (TCM) records via text mining methods with the aim of identifying the key medicines, prescriptions and formulae when taking patients' TCM syndromes into consideration. After structuring the TCM syndrome variables according to the diagnostic scale of TCM syndrome elements, a Chinese segmentation method was adopted at the initial stage during text ...


Data-Driven Based State Recognition Method for Airliner Fuselage Join

2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC), 2018

To accurately recognize the fuselage joining state is a great challenge in automation process. The accumulation of joining data and the development of machine learning methods have greatly facilitated it. In this paper, a data- driven based state recognition method is proposed for airliner fuselage join. Firstly, combined with the actual process of the fuselage join, the state labeling of ...


An Improved Weighted Local Linear Embedding Algorithm

2018 14th International Conference on Computational Intelligence and Security (CIS), 2018

Local linear embedding has the characteristics of nonlinearity and simple implementation, but it cannot accurately handle the selection of neighborhoods under the conditions of noise, large curvature and sparse sampling. To solve this problem, an improved weighted local linear embedding method (WLE-LLE) is proposed. In WLE-LLE, the dimensionality reduction objective function is reconstructed by utilizing Laplacian Eigenmaps, which can effectively ...


A Novel Deep Learning Framework by Combination of Subspace-Based Feature Extraction and Convolutional Neural Networks for Hyperspectral Images Classification

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018

Approaches based on deep learning have gained an increased attention in the recent years in particular Remote Sensing. Convolutional Neural Networks (CNNs) as one of these deep learning techniques has demonstrated remarkable performance in visual recognition applications. However, using well-known pre- train models such as GoogleNet and VGGNet in the area of hyperspectral image classification due to the high dimensionality ...


Energy-Saving Algorithm with Dimension Reduction on the Uplink for Multimedia Push

2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), 2017

Distributed Principal Component Analysis (DPCA) is a useful tool to make a trade-off between the cost of communication and data error. In this paper, DCPA is used for dimension reduction to compress data on the uplink. There is no information but the data error threshold in the base station (BS). Based on it, the relationship between the data error threshold ...


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Educational Resources on Dimensionality Reduction

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IEEE-USA E-Books

  • An Analysis and Research of Type-2 Diabetes TCM Records Based On Text Mining

    This paper analyzes 152 type-2 diabetes Traditional Chinese Medicine (TCM) records via text mining methods with the aim of identifying the key medicines, prescriptions and formulae when taking patients' TCM syndromes into consideration. After structuring the TCM syndrome variables according to the diagnostic scale of TCM syndrome elements, a Chinese segmentation method was adopted at the initial stage during text mining. K-Medoids method was selected to cluster the TCM records. Eventually, a FP-Growth algorithm was applied in this paper for the purpose of discovering hidden relationships between syndromes and prescriptions whose confidence values are relatively higher. In terms of the results, this research has shown 71% accuracy in test sets. Additionally, all three senior TCM doctors deem the result feasible and acceptable.

  • Data-Driven Based State Recognition Method for Airliner Fuselage Join

    To accurately recognize the fuselage joining state is a great challenge in automation process. The accumulation of joining data and the development of machine learning methods have greatly facilitated it. In this paper, a data- driven based state recognition method is proposed for airliner fuselage join. Firstly, combined with the actual process of the fuselage join, the state labeling of joining data is completed by means of PCA and k-means clustering. Secondly, a Random Forest classifier is trained based on the labeled data, and the accuracy of the classifier is evaluated by cross validation. The results show that the proposed method can effectively label different fuselage joining states and achieve accurate state recognition for fuselage join.

  • An Improved Weighted Local Linear Embedding Algorithm

    Local linear embedding has the characteristics of nonlinearity and simple implementation, but it cannot accurately handle the selection of neighborhoods under the conditions of noise, large curvature and sparse sampling. To solve this problem, an improved weighted local linear embedding method (WLE-LLE) is proposed. In WLE-LLE, the dimensionality reduction objective function is reconstructed by utilizing Laplacian Eigenmaps, which can effectively represent the manifold structure of nonlinear data. Theoretical analyses show the proposed method is better than LLE algorithm in preserving the original manifold structure of the data. And numerical experiments show its classification recognition rate is greatly improved, which is 2%-8% higher than LLE.

  • A Novel Deep Learning Framework by Combination of Subspace-Based Feature Extraction and Convolutional Neural Networks for Hyperspectral Images Classification

    Approaches based on deep learning have gained an increased attention in the recent years in particular Remote Sensing. Convolutional Neural Networks (CNNs) as one of these deep learning techniques has demonstrated remarkable performance in visual recognition applications. However, using well-known pre- train models such as GoogleNet and VGGNet in the area of hyperspectral image classification due to the high dimensionality and the insufficient training samples is intractable. The current study proposed a new and fixes CNN architecture for two real hyperspectral data sets. To overcome curse of dimensionality we perform a subspace-based feature extraction method by calculating the orthonormal basis of correlation matrix for each class to reduce the dimensionality of hyperspectral images and increasing signal to noise ratio. This framework combines the proposed CNN architecture and subspace reduction method to prepare informative features (from subspace method) and designing optimized CNN by considering limitation of training samples. Also, feature generated by subspace reduction method is compatible by the nature of class based CNNs and a logistic regression as a classifier in the last layer of proposed architecture. Experimental results from two real and well-known hyperspectral images, the Indiana Pines and the Pavia University scenes show that the proposed strategy leads to a performance improvement, as opposed to using the original data and conventional feature extraction strategies which have been employed during the recent approaches. The classification overall accuracy of ca. 98.1% and 98.3% were obtained in Indian Pine and Pavia University respectively.

  • Energy-Saving Algorithm with Dimension Reduction on the Uplink for Multimedia Push

    Distributed Principal Component Analysis (DPCA) is a useful tool to make a trade-off between the cost of communication and data error. In this paper, DCPA is used for dimension reduction to compress data on the uplink. There is no information but the data error threshold in the base station (BS). Based on it, the relationship between the data error threshold in the BS and in user equipments is proved and revised. The revised relationship is very simply and irrelevant to the quantity of user equipments. After that, a novel energy- saving algorithm on the uplink for multimedia push is proposed. In the algorithm, the dimension reduction is applied in every user equipment with the data error threshold, and the compressed data matrixes would be transmitted to the BS. In simulation, the energy could be saved while using the algorithm, and the effect is changed with different data sets. Afterwards, the upper bound about the ratio of energy saving is estimated.

  • Content based image retrieval of remote sensing images based on deep features

    This paper presents the results of applying deep features to the problem of content based image retieval of remote sensing images. Extraction of deep features from the last layers of a trained convolutional neural network from deep learning approaches demonstrates a higher performance than feature extraction using shallow methods. In this paper we used deep features obtained from a fine tuned convolutional neural network and we also focused on experiments of dimension reduction methods of these deep features. We test these methods using UCM Merced and RSSCN7 datasets. Despite their shorter length deep features obtained as a result of dimension reduction methods, are shown to achieve higher performance of content-based retrieval.

  • RADM:Real-Time Anomaly Detection in Multivariate Time Series Based on Bayesian Network

    Aiming at the anomaly detection in multivariate time series(MTS), we propose a real-time anomaly detection algorithm in MTS based on Hierarchical Temporal Memory(HTM) and Bayesian Network(BN), called RADM. First of all, we use HTM model to evaluate the real-time anomalies of each univariate time series(UTS) in MTS. Secondly, a model of anomalous state detection in MTS based on Naive Bayesian is designed to analyze the validity of the above MTS. Lastly, considering the real-time monitoring cases of the system states of terminal nodes in Cloud Platform, we utilize ternary time series of CPU utilization, Network speed and Memory occupancy ratio as data samples, and through the experimental simulation, we verify that RADM proposed in this paper can take advantage of the specific relevance in MTS and make a more effective judgment on the system anomalies.

  • Feature Selection Using Autoencoders

    Feature selection plays a vital role in improving the generalization accuracy in many classification tasks where datasets are high-dimensional. In feature selection, a minimal subset of relevant as well as non-redundant features is selected. Autoencoders are used to represent the datasets from original feature space to a reduced and more informative feature space. In this paper, we propose a novel approach for feature selection by traversing back the autoencoders through more probable links. Experiments on five publicly available large datasets show that our approach gives significant gains in accuracy over most of the state-of-the-art feature selection methods.

  • Analyzing dimensionality reduction with softmax discriminant classifier for epilepsy classification

    Due to the unpredictable interruptions in the normal brain functions, recurrent seizures occur and this type of disorder is termed as epilepsy. The motor, sensory and other autonomic functions of the brain are severely affected by seizures. Also the memory, state of consciousness, emotional behaviour is equally affected by seizures. A most efficient and versatile equipment for the diagnosis of this syndrome is Electroencephalography (EEG). In the EEG signal, 2 kinds of abnormal activities can be sharply observed as ictal and interictal. Ictal activities happen when an epileptic seizure occurs and interictal activities happen in between the seizures. To avoid the misdiagnosis, the patient's ictal EEG is quite significant for the analysis. Due to the unforeseen and sudden occurrence of seizures, there is a huge difficulty in recording the EEG signals. The long term EEG recordings which are continuous in nature has a lot of data to be processed and so dimensionality reduction techniques like Factor Analysis and Singular Value Decomposition (SVD) are utilized to reduce the dimensions. The dimensionally reduced values are then classified with the help of Softmax Discriminant Classifier (SDC) for epilepsy classification from EEG signals. Results show that when Factor Analysis is classified with SDC, an average classification accuracy of 95.04% is obtained and when SVD is classified with SDC, an average classification accuracy of 96.42% is obtained.

  • Factor Analysis and Weighted KNN Classifier for Epilepsy Classification from EEG signals

    Many people suffering from epilepsy can be controlled with the help of anti -epileptic drugs. For some people remission is possible and for some people, who suffer from epilepsy, some sort of specialist overview is required, however, to co-ordinate both the treatment and diagnosis, the general practitioner plays a major role. Epilepsy in general can be controlled for most patients, provided they maintain a healthy lifestyle. People suffering from epilepsy should not unnecessarily strain themselves and it is good to avoid excess alcohol, dehydration, flash lights and illicit drugs. Based on the Electroencephalography (EEG) signal analysis, epilepsy can be easily detected. For epileptic patients, during a particular seizure, the EEG signals shows a specific pattern and it is different in terms of space, frequency and time when compared to the normal state of the brain. In this paper, the dimensions of the EEG are reduced with the help of Factor Analysis and then it is classified with the help of Weighted K Nearest Neighbour Classifier.



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