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Other applications of machine learning (MLR-APPL)

Semi-Supervised Classification via Both Label and Side Information

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Authors:
Rui Zhang, Feiping Nie, Xuelong Li
Submitted On:
28 February 2017 - 9:36pm
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[1] Rui Zhang, Feiping Nie, Xuelong Li, "Semi-Supervised Classification via Both Label and Side Information", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1526. Accessed: Jul. 23, 2019.
@article{1526-17,
url = {http://sigport.org/1526},
author = {Rui Zhang; Feiping Nie; Xuelong Li },
publisher = {IEEE SigPort},
title = {Semi-Supervised Classification via Both Label and Side Information},
year = {2017} }
TY - EJOUR
T1 - Semi-Supervised Classification via Both Label and Side Information
AU - Rui Zhang; Feiping Nie; Xuelong Li
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1526
ER -
Rui Zhang, Feiping Nie, Xuelong Li. (2017). Semi-Supervised Classification via Both Label and Side Information. IEEE SigPort. http://sigport.org/1526
Rui Zhang, Feiping Nie, Xuelong Li, 2017. Semi-Supervised Classification via Both Label and Side Information. Available at: http://sigport.org/1526.
Rui Zhang, Feiping Nie, Xuelong Li. (2017). "Semi-Supervised Classification via Both Label and Side Information." Web.
1. Rui Zhang, Feiping Nie, Xuelong Li. Semi-Supervised Classification via Both Label and Side Information [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1526

HEARTMATE: AUTOMATED INTEGRATED ANOMALY ANALYSIS FOR EFFECTIVE REMOTE CARDIAC HEALTH MANAGEMENT


Remote cardiac health management is an important healthcare application. We have developed Heartmate that enables basic screening of cardiac health using low cost sensors or smartphone-inbuilt sensors without manual intervention. It consists of robust denoising algorithm along with effective anomaly analytics for physiological signals. Heartmate identifies and eliminates signal corruption as well as detects cardiac anomaly condition from physiological cardiac signals like heart sound or phonocardiogram (PCG) and photoplethysmogram (PPG).

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Authors:
Arijit Ukil, Soma Bandyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal, Ayan Mukherjee
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28 February 2017 - 1:17am
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Poster for the demo to be shown at ICASSP 2017

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[1] Arijit Ukil, Soma Bandyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal, Ayan Mukherjee, "HEARTMATE: AUTOMATED INTEGRATED ANOMALY ANALYSIS FOR EFFECTIVE REMOTE CARDIAC HEALTH MANAGEMENT", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1480. Accessed: Jul. 23, 2019.
@article{1480-17,
url = {http://sigport.org/1480},
author = {Arijit Ukil; Soma Bandyopadhyay; Chetanya Puri; Rituraj Singh; Arpan Pal; Ayan Mukherjee },
publisher = {IEEE SigPort},
title = {HEARTMATE: AUTOMATED INTEGRATED ANOMALY ANALYSIS FOR EFFECTIVE REMOTE CARDIAC HEALTH MANAGEMENT},
year = {2017} }
TY - EJOUR
T1 - HEARTMATE: AUTOMATED INTEGRATED ANOMALY ANALYSIS FOR EFFECTIVE REMOTE CARDIAC HEALTH MANAGEMENT
AU - Arijit Ukil; Soma Bandyopadhyay; Chetanya Puri; Rituraj Singh; Arpan Pal; Ayan Mukherjee
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1480
ER -
Arijit Ukil, Soma Bandyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal, Ayan Mukherjee. (2017). HEARTMATE: AUTOMATED INTEGRATED ANOMALY ANALYSIS FOR EFFECTIVE REMOTE CARDIAC HEALTH MANAGEMENT. IEEE SigPort. http://sigport.org/1480
Arijit Ukil, Soma Bandyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal, Ayan Mukherjee, 2017. HEARTMATE: AUTOMATED INTEGRATED ANOMALY ANALYSIS FOR EFFECTIVE REMOTE CARDIAC HEALTH MANAGEMENT. Available at: http://sigport.org/1480.
Arijit Ukil, Soma Bandyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal, Ayan Mukherjee. (2017). "HEARTMATE: AUTOMATED INTEGRATED ANOMALY ANALYSIS FOR EFFECTIVE REMOTE CARDIAC HEALTH MANAGEMENT." Web.
1. Arijit Ukil, Soma Bandyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal, Ayan Mukherjee. HEARTMATE: AUTOMATED INTEGRATED ANOMALY ANALYSIS FOR EFFECTIVE REMOTE CARDIAC HEALTH MANAGEMENT [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1480

Data Mining the Underlying Trust in the US Congress


In this paper, we mine the US congress voting records to extract the latent information about the trust among congress members. In particular, we model the Senate as a social network and the voting process as a set of outcomes of the underlying opinion dynamics which we assume follow a corrupted DeGroot model. The transition matrix in the opinion dynamics model is the trust matrix among Senators that we estimate.

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Authors:
Sissi Xiaoxiao Wu, Hoi-To Wai and Anna Scaglione
Submitted On:
6 December 2016 - 11:29pm
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[1] Sissi Xiaoxiao Wu, Hoi-To Wai and Anna Scaglione, "Data Mining the Underlying Trust in the US Congress", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1393. Accessed: Jul. 23, 2019.
@article{1393-16,
url = {http://sigport.org/1393},
author = {Sissi Xiaoxiao Wu; Hoi-To Wai and Anna Scaglione },
publisher = {IEEE SigPort},
title = {Data Mining the Underlying Trust in the US Congress},
year = {2016} }
TY - EJOUR
T1 - Data Mining the Underlying Trust in the US Congress
AU - Sissi Xiaoxiao Wu; Hoi-To Wai and Anna Scaglione
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1393
ER -
Sissi Xiaoxiao Wu, Hoi-To Wai and Anna Scaglione. (2016). Data Mining the Underlying Trust in the US Congress. IEEE SigPort. http://sigport.org/1393
Sissi Xiaoxiao Wu, Hoi-To Wai and Anna Scaglione, 2016. Data Mining the Underlying Trust in the US Congress. Available at: http://sigport.org/1393.
Sissi Xiaoxiao Wu, Hoi-To Wai and Anna Scaglione. (2016). "Data Mining the Underlying Trust in the US Congress." Web.
1. Sissi Xiaoxiao Wu, Hoi-To Wai and Anna Scaglione. Data Mining the Underlying Trust in the US Congress [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1393

Coupled Dictionary Learning for Multi-modal Image Super-resolution

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Authors:
Jo\~ao Mota, Nikos Deligiannis, Miguel Rodrigues
Submitted On:
1 December 2016 - 7:25am
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[1] Jo\~ao Mota, Nikos Deligiannis, Miguel Rodrigues, "Coupled Dictionary Learning for Multi-modal Image Super-resolution", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1326. Accessed: Jul. 23, 2019.
@article{1326-16,
url = {http://sigport.org/1326},
author = {Jo\~ao Mota; Nikos Deligiannis; Miguel Rodrigues },
publisher = {IEEE SigPort},
title = {Coupled Dictionary Learning for Multi-modal Image Super-resolution},
year = {2016} }
TY - EJOUR
T1 - Coupled Dictionary Learning for Multi-modal Image Super-resolution
AU - Jo\~ao Mota; Nikos Deligiannis; Miguel Rodrigues
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1326
ER -
Jo\~ao Mota, Nikos Deligiannis, Miguel Rodrigues. (2016). Coupled Dictionary Learning for Multi-modal Image Super-resolution. IEEE SigPort. http://sigport.org/1326
Jo\~ao Mota, Nikos Deligiannis, Miguel Rodrigues, 2016. Coupled Dictionary Learning for Multi-modal Image Super-resolution. Available at: http://sigport.org/1326.
Jo\~ao Mota, Nikos Deligiannis, Miguel Rodrigues. (2016). "Coupled Dictionary Learning for Multi-modal Image Super-resolution." Web.
1. Jo\~ao Mota, Nikos Deligiannis, Miguel Rodrigues. Coupled Dictionary Learning for Multi-modal Image Super-resolution [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1326

Classification between normal and adventitious lung sounds using deep neural network

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13 October 2016 - 9:57pm
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Oral presentation

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[1] , "Classification between normal and adventitious lung sounds using deep neural network", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1176. Accessed: Jul. 23, 2019.
@article{1176-16,
url = {http://sigport.org/1176},
author = { },
publisher = {IEEE SigPort},
title = {Classification between normal and adventitious lung sounds using deep neural network},
year = {2016} }
TY - EJOUR
T1 - Classification between normal and adventitious lung sounds using deep neural network
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1176
ER -
. (2016). Classification between normal and adventitious lung sounds using deep neural network. IEEE SigPort. http://sigport.org/1176
, 2016. Classification between normal and adventitious lung sounds using deep neural network. Available at: http://sigport.org/1176.
. (2016). "Classification between normal and adventitious lung sounds using deep neural network." Web.
1. . Classification between normal and adventitious lung sounds using deep neural network [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1176

Mood State Prediction from Speech of Varying Acoustic Quality for Individuals with Bipolar Disorder


Speech contains patterns that can be altered by the mood of an individual. There is an increasing focus on automated and distributed methods to collect and monitor speech from large groups of patients suffering from mental health disorders. However, as the scope of these collections increases, the variability in the data also increases. This variability is due in part to the range in the quality of the devices, which in turn affects the quality of the recorded data, negatively impacting the accuracy of automatic assessment.

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Authors:
Emily Mower Provost, Melvin McInnis
Submitted On:
27 March 2016 - 3:20pm
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ICASSP 2016 Final.pdf

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[1] Emily Mower Provost, Melvin McInnis, "Mood State Prediction from Speech of Varying Acoustic Quality for Individuals with Bipolar Disorder", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1056. Accessed: Jul. 23, 2019.
@article{1056-16,
url = {http://sigport.org/1056},
author = {Emily Mower Provost; Melvin McInnis },
publisher = {IEEE SigPort},
title = {Mood State Prediction from Speech of Varying Acoustic Quality for Individuals with Bipolar Disorder},
year = {2016} }
TY - EJOUR
T1 - Mood State Prediction from Speech of Varying Acoustic Quality for Individuals with Bipolar Disorder
AU - Emily Mower Provost; Melvin McInnis
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1056
ER -
Emily Mower Provost, Melvin McInnis. (2016). Mood State Prediction from Speech of Varying Acoustic Quality for Individuals with Bipolar Disorder. IEEE SigPort. http://sigport.org/1056
Emily Mower Provost, Melvin McInnis, 2016. Mood State Prediction from Speech of Varying Acoustic Quality for Individuals with Bipolar Disorder. Available at: http://sigport.org/1056.
Emily Mower Provost, Melvin McInnis. (2016). "Mood State Prediction from Speech of Varying Acoustic Quality for Individuals with Bipolar Disorder." Web.
1. Emily Mower Provost, Melvin McInnis. Mood State Prediction from Speech of Varying Acoustic Quality for Individuals with Bipolar Disorder [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1056

Active Learning for Magnetic Resonance Image Quality Assessment


In medical imaging, the acquired images are usually analyzed by a human observer and rated with respect to a diagnostic question. However, this procedure is time-demanding and expensive. Furthermore, the lack of a reference image makes this task challenging. In order to support the human observer in assessing image quality and to ensure an objective evaluation, we extend in this paper our previous no-reference magnetic resonance (MR) image quality assessment system with an active learning loop to reduce the amount of necessary labeled training data.

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Authors:
Annika Liebgott, Thomas Küstner, Sergios Gatidis, Fritz Schick, Bin Yang
Submitted On:
21 March 2016 - 9:02am
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Kuestner_ICASSP_poster_FINAL.pdf

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[1] Annika Liebgott, Thomas Küstner, Sergios Gatidis, Fritz Schick, Bin Yang, "Active Learning for Magnetic Resonance Image Quality Assessment", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/924. Accessed: Jul. 23, 2019.
@article{924-16,
url = {http://sigport.org/924},
author = {Annika Liebgott; Thomas Küstner; Sergios Gatidis; Fritz Schick; Bin Yang },
publisher = {IEEE SigPort},
title = {Active Learning for Magnetic Resonance Image Quality Assessment},
year = {2016} }
TY - EJOUR
T1 - Active Learning for Magnetic Resonance Image Quality Assessment
AU - Annika Liebgott; Thomas Küstner; Sergios Gatidis; Fritz Schick; Bin Yang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/924
ER -
Annika Liebgott, Thomas Küstner, Sergios Gatidis, Fritz Schick, Bin Yang. (2016). Active Learning for Magnetic Resonance Image Quality Assessment. IEEE SigPort. http://sigport.org/924
Annika Liebgott, Thomas Küstner, Sergios Gatidis, Fritz Schick, Bin Yang, 2016. Active Learning for Magnetic Resonance Image Quality Assessment. Available at: http://sigport.org/924.
Annika Liebgott, Thomas Küstner, Sergios Gatidis, Fritz Schick, Bin Yang. (2016). "Active Learning for Magnetic Resonance Image Quality Assessment." Web.
1. Annika Liebgott, Thomas Küstner, Sergios Gatidis, Fritz Schick, Bin Yang. Active Learning for Magnetic Resonance Image Quality Assessment [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/924

Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics


Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics

In this paper, we present Discriminant Correlation Analysis (DCA), a feature level fusion technique that incorporates the class associations in correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pair-wise correlations across the two feature sets, and at the same time, eliminating the between-class correlations and restricting the correlations to be within classes.

[1] Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi, "Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/828. Accessed: Jul. 23, 2019.
@article{828-16,
url = {http://sigport.org/828},
author = {Mohammad Haghighat; Mohamed Abdel-Mottaleb; Wadee Alhalabi },
publisher = {IEEE SigPort},
title = {Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics},
year = {2016} }
TY - EJOUR
T1 - Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics
AU - Mohammad Haghighat; Mohamed Abdel-Mottaleb; Wadee Alhalabi
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/828
ER -
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. (2016). Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics. IEEE SigPort. http://sigport.org/828
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi, 2016. Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics. Available at: http://sigport.org/828.
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. (2016). "Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics." Web.
1. Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/828

Theoretical guarantees for Poisson disk sampling using pair correlation function


Theoretical guarantees for Poisson disk sampling using pair correlation function

In this paper, we study the problem of generating uniform random
point samples on a domain of d-dimensional space based on a minimum
distance criterion between point samples (Poisson-disk sampling
or PDS). First, we formally define PDS via the pair correlation
function (PCF) to quantitatively evaluate properties of the sampling
process. Surprisingly, none of the existing PDS techniques
satisfy both uniformity and minimum distance criterion, simultaneously.
These approaches typically create an approximate PDS with

Paper Details

Authors:
Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Pramod K Varshney
Submitted On:
19 March 2016 - 11:20am
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[1] Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Pramod K Varshney, "Theoretical guarantees for Poisson disk sampling using pair correlation function", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/817. Accessed: Jul. 23, 2019.
@article{817-16,
url = {http://sigport.org/817},
author = {Bhavya Kailkhura; Jayaraman J. Thiagarajan; Peer-Timo Bremer; Pramod K Varshney },
publisher = {IEEE SigPort},
title = {Theoretical guarantees for Poisson disk sampling using pair correlation function},
year = {2016} }
TY - EJOUR
T1 - Theoretical guarantees for Poisson disk sampling using pair correlation function
AU - Bhavya Kailkhura; Jayaraman J. Thiagarajan; Peer-Timo Bremer; Pramod K Varshney
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/817
ER -
Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Pramod K Varshney. (2016). Theoretical guarantees for Poisson disk sampling using pair correlation function. IEEE SigPort. http://sigport.org/817
Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Pramod K Varshney, 2016. Theoretical guarantees for Poisson disk sampling using pair correlation function. Available at: http://sigport.org/817.
Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Pramod K Varshney. (2016). "Theoretical guarantees for Poisson disk sampling using pair correlation function." Web.
1. Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Pramod K Varshney. Theoretical guarantees for Poisson disk sampling using pair correlation function [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/817

Visualizations Relevant to the User by Multi-View Latent Variable Factorization


A main goal of data visualization is to find, from among all the available alternatives, mappings to the 2D/3D display which are relevant to the user. Assuming user interaction data, or other auxiliary data about the items or their relationships, the goal is to identify which aspects in the primary data support the user’s input and, equally importantly, which aspects of the user’s potentially noisy input have support in the primary data.

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Authors:
Seppo Virtanen, Homayun Afrabandpey, Samuel Kaski
Submitted On:
22 March 2016 - 11:34pm
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Visualizations Relevant to the User by Multi-View Latent Variable Factorization.pdf

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[1] Seppo Virtanen, Homayun Afrabandpey, Samuel Kaski, "Visualizations Relevant to the User by Multi-View Latent Variable Factorization ", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/630. Accessed: Jul. 23, 2019.
@article{630-16,
url = {http://sigport.org/630},
author = {Seppo Virtanen; Homayun Afrabandpey; Samuel Kaski },
publisher = {IEEE SigPort},
title = {Visualizations Relevant to the User by Multi-View Latent Variable Factorization },
year = {2016} }
TY - EJOUR
T1 - Visualizations Relevant to the User by Multi-View Latent Variable Factorization
AU - Seppo Virtanen; Homayun Afrabandpey; Samuel Kaski
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/630
ER -
Seppo Virtanen, Homayun Afrabandpey, Samuel Kaski. (2016). Visualizations Relevant to the User by Multi-View Latent Variable Factorization . IEEE SigPort. http://sigport.org/630
Seppo Virtanen, Homayun Afrabandpey, Samuel Kaski, 2016. Visualizations Relevant to the User by Multi-View Latent Variable Factorization . Available at: http://sigport.org/630.
Seppo Virtanen, Homayun Afrabandpey, Samuel Kaski. (2016). "Visualizations Relevant to the User by Multi-View Latent Variable Factorization ." Web.
1. Seppo Virtanen, Homayun Afrabandpey, Samuel Kaski. Visualizations Relevant to the User by Multi-View Latent Variable Factorization [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/630

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