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Other applications of machine learning (MLR-APPL)

Learning a Low-coherence Dictionary to Address Spectral Variability for Hyperspectral Unmixing

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Authors:
Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu
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16 September 2017 - 4:51am
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hyperspectral unmixing

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[1] Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu, "Learning a Low-coherence Dictionary to Address Spectral Variability for Hyperspectral Unmixing", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2184. Accessed: Sep. 20, 2017.
@article{2184-17,
url = {http://sigport.org/2184},
author = {Danfeng Hong; Naoto Yokoya; Jocelyn Chanussot; Xiao Xiang Zhu },
publisher = {IEEE SigPort},
title = {Learning a Low-coherence Dictionary to Address Spectral Variability for Hyperspectral Unmixing},
year = {2017} }
TY - EJOUR
T1 - Learning a Low-coherence Dictionary to Address Spectral Variability for Hyperspectral Unmixing
AU - Danfeng Hong; Naoto Yokoya; Jocelyn Chanussot; Xiao Xiang Zhu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2184
ER -
Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu. (2017). Learning a Low-coherence Dictionary to Address Spectral Variability for Hyperspectral Unmixing. IEEE SigPort. http://sigport.org/2184
Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu, 2017. Learning a Low-coherence Dictionary to Address Spectral Variability for Hyperspectral Unmixing. Available at: http://sigport.org/2184.
Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu. (2017). "Learning a Low-coherence Dictionary to Address Spectral Variability for Hyperspectral Unmixing." Web.
1. Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu. Learning a Low-coherence Dictionary to Address Spectral Variability for Hyperspectral Unmixing [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2184

Greedy Deep Transform Learning


We introduce deep transform learning – a new
tool for deep learning. Deeper representation is learnt by
stacking one transform after another. The learning proceeds in
a greedy way. The first layer learns the transform and features
from the input training samples. Subsequent layers use the
features (after activation) from the previous layers as training
input. Experiments have been carried out with other deep
representation learning tools – deep dictionary learning,
stacked denoising autoencoder, deep belief network and PCANet

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Authors:
Jyoti Maggu, Angshul Majumdar
Submitted On:
18 September 2017 - 1:57pm
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ICIP_greedyDTL.pdf

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[1] Jyoti Maggu, Angshul Majumdar, "Greedy Deep Transform Learning", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2180. Accessed: Sep. 20, 2017.
@article{2180-17,
url = {http://sigport.org/2180},
author = {Jyoti Maggu; Angshul Majumdar },
publisher = {IEEE SigPort},
title = {Greedy Deep Transform Learning},
year = {2017} }
TY - EJOUR
T1 - Greedy Deep Transform Learning
AU - Jyoti Maggu; Angshul Majumdar
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2180
ER -
Jyoti Maggu, Angshul Majumdar. (2017). Greedy Deep Transform Learning. IEEE SigPort. http://sigport.org/2180
Jyoti Maggu, Angshul Majumdar, 2017. Greedy Deep Transform Learning. Available at: http://sigport.org/2180.
Jyoti Maggu, Angshul Majumdar. (2017). "Greedy Deep Transform Learning." Web.
1. Jyoti Maggu, Angshul Majumdar. Greedy Deep Transform Learning [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2180

Biobotic Motion and Behavior Analysis in Response to Directional Neurostimulation


This paper presents preliminary results for motion behavior analysis of Madagascar hissing cockroach biobots subject to stochastic and periodic neurostimulation pulses corresponding to randomly applied right and left turn, and move forward commands. We present our experimental setup and propose an unguided search strategy based stimulation profile designed for exploration of unknown environments. We study a probabilistic motion model fitted to the trajectories of biobots, perturbed from their natural motion by the stimulation pulses.

Poster.pdf

PDF icon Poster.pdf (475 downloads)

Paper Details

Authors:
Alireza Dirafzoon, Tahmid Latif, Fengyuan Gong, Mihail Sichitiu, Alper Bozkurt, Edgar Lobaton
Submitted On:
6 March 2017 - 7:40am
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Poster.pdf

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[1] Alireza Dirafzoon, Tahmid Latif, Fengyuan Gong, Mihail Sichitiu, Alper Bozkurt, Edgar Lobaton, "Biobotic Motion and Behavior Analysis in Response to Directional Neurostimulation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1646. Accessed: Sep. 20, 2017.
@article{1646-17,
url = {http://sigport.org/1646},
author = {Alireza Dirafzoon; Tahmid Latif; Fengyuan Gong; Mihail Sichitiu; Alper Bozkurt; Edgar Lobaton },
publisher = {IEEE SigPort},
title = {Biobotic Motion and Behavior Analysis in Response to Directional Neurostimulation},
year = {2017} }
TY - EJOUR
T1 - Biobotic Motion and Behavior Analysis in Response to Directional Neurostimulation
AU - Alireza Dirafzoon; Tahmid Latif; Fengyuan Gong; Mihail Sichitiu; Alper Bozkurt; Edgar Lobaton
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1646
ER -
Alireza Dirafzoon, Tahmid Latif, Fengyuan Gong, Mihail Sichitiu, Alper Bozkurt, Edgar Lobaton. (2017). Biobotic Motion and Behavior Analysis in Response to Directional Neurostimulation. IEEE SigPort. http://sigport.org/1646
Alireza Dirafzoon, Tahmid Latif, Fengyuan Gong, Mihail Sichitiu, Alper Bozkurt, Edgar Lobaton, 2017. Biobotic Motion and Behavior Analysis in Response to Directional Neurostimulation. Available at: http://sigport.org/1646.
Alireza Dirafzoon, Tahmid Latif, Fengyuan Gong, Mihail Sichitiu, Alper Bozkurt, Edgar Lobaton. (2017). "Biobotic Motion and Behavior Analysis in Response to Directional Neurostimulation." Web.
1. Alireza Dirafzoon, Tahmid Latif, Fengyuan Gong, Mihail Sichitiu, Alper Bozkurt, Edgar Lobaton. Biobotic Motion and Behavior Analysis in Response to Directional Neurostimulation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1646

mbedded Clustering via Robust Orthogonal Least Square Discriminant Analysis

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Authors:
Rui Zhang, Feiping Nie, Xuelong Li
Submitted On:
2 March 2017 - 4:13pm
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beamer of EC

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[1] Rui Zhang, Feiping Nie, Xuelong Li, "mbedded Clustering via Robust Orthogonal Least Square Discriminant Analysis", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1596. Accessed: Sep. 20, 2017.
@article{1596-17,
url = {http://sigport.org/1596},
author = {Rui Zhang; Feiping Nie; Xuelong Li },
publisher = {IEEE SigPort},
title = {mbedded Clustering via Robust Orthogonal Least Square Discriminant Analysis},
year = {2017} }
TY - EJOUR
T1 - mbedded Clustering via Robust Orthogonal Least Square Discriminant Analysis
AU - Rui Zhang; Feiping Nie; Xuelong Li
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1596
ER -
Rui Zhang, Feiping Nie, Xuelong Li. (2017). mbedded Clustering via Robust Orthogonal Least Square Discriminant Analysis. IEEE SigPort. http://sigport.org/1596
Rui Zhang, Feiping Nie, Xuelong Li, 2017. mbedded Clustering via Robust Orthogonal Least Square Discriminant Analysis. Available at: http://sigport.org/1596.
Rui Zhang, Feiping Nie, Xuelong Li. (2017). "mbedded Clustering via Robust Orthogonal Least Square Discriminant Analysis." Web.
1. Rui Zhang, Feiping Nie, Xuelong Li. mbedded Clustering via Robust Orthogonal Least Square Discriminant Analysis [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1596

A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT


Practical limitations on the duration of individual fMRI scans have led neuroscientist to consider the aggregation of data from multiple subjects. Differences in anatomical structures and functional topographies of brains require aligning data across subjects. Existing functional alignment methods serve as a preprocessing step that allows subsequent statistical methods to learn from the aggregated multi-subject data. Despite their success, current alignment methods do not leverage the labeled data used in the subsequent methods.

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Authors:
Javier S. Turek, Theodore L. Willke, Po-Hsuan Chen, Peter J. Ramadge
Submitted On:
2 March 2017 - 12:56pm
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Semi-Supervised fMRI Functional Alignment

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[1] Javier S. Turek, Theodore L. Willke, Po-Hsuan Chen, Peter J. Ramadge, "A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1587. Accessed: Sep. 20, 2017.
@article{1587-17,
url = {http://sigport.org/1587},
author = {Javier S. Turek; Theodore L. Willke; Po-Hsuan Chen; Peter J. Ramadge },
publisher = {IEEE SigPort},
title = {A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT},
year = {2017} }
TY - EJOUR
T1 - A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT
AU - Javier S. Turek; Theodore L. Willke; Po-Hsuan Chen; Peter J. Ramadge
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1587
ER -
Javier S. Turek, Theodore L. Willke, Po-Hsuan Chen, Peter J. Ramadge. (2017). A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT. IEEE SigPort. http://sigport.org/1587
Javier S. Turek, Theodore L. Willke, Po-Hsuan Chen, Peter J. Ramadge, 2017. A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT. Available at: http://sigport.org/1587.
Javier S. Turek, Theodore L. Willke, Po-Hsuan Chen, Peter J. Ramadge. (2017). "A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT." Web.
1. Javier S. Turek, Theodore L. Willke, Po-Hsuan Chen, Peter J. Ramadge. A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1587

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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poster of EC

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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: Sep. 20, 2017.
@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
Submitted On:
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: Sep. 20, 2017.
@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.

Paper Details

Authors:
Sissi Xiaoxiao Wu, Hoi-To Wai and Anna Scaglione
Submitted On:
6 December 2016 - 11:29pm
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GlobalSipPresent.pdf

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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: Sep. 20, 2017.
@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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Slides_CDL_SR_GlobalSIP2016

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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: Sep. 20, 2017.
@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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Submitted On:
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: Sep. 20, 2017.
@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

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