Sorry, you need to enable JavaScript to visit this website.

Pattern recognition and classification (MLR-PATT)

INDUCTIVE CONFORMAL PREDICTOR FOR SPARSE CODING CLASSIFIERS: APPLICATIONS TO IMAGE CLASSIFICATION


Conformal prediction uses the degree of strangeness (nonconformity) of new data instances to determine the confidence values of new predictions. We propose an inductive conformal predictor for sparse coding classifiers, referred to as ICP-SCC. Our contribution is twofold: first, we present two nonconformitymeasures that produce reliable confidence values; second, we propose a batchmode active learning algorithm within the conformal prediction framework to improve classification performance by selecting training instances based on two criteria, informativeness and diversity.

Paper Details

Authors:
Kenneth E. Barner
Submitted On:
14 May 2019 - 10:41am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Poster ICASSP 2019

(26)

Subscribe

[1] Kenneth E. Barner, "INDUCTIVE CONFORMAL PREDICTOR FOR SPARSE CODING CLASSIFIERS: APPLICATIONS TO IMAGE CLASSIFICATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4512. Accessed: Aug. 18, 2019.
@article{4512-19,
url = {http://sigport.org/4512},
author = {Kenneth E. Barner },
publisher = {IEEE SigPort},
title = {INDUCTIVE CONFORMAL PREDICTOR FOR SPARSE CODING CLASSIFIERS: APPLICATIONS TO IMAGE CLASSIFICATION},
year = {2019} }
TY - EJOUR
T1 - INDUCTIVE CONFORMAL PREDICTOR FOR SPARSE CODING CLASSIFIERS: APPLICATIONS TO IMAGE CLASSIFICATION
AU - Kenneth E. Barner
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4512
ER -
Kenneth E. Barner. (2019). INDUCTIVE CONFORMAL PREDICTOR FOR SPARSE CODING CLASSIFIERS: APPLICATIONS TO IMAGE CLASSIFICATION. IEEE SigPort. http://sigport.org/4512
Kenneth E. Barner, 2019. INDUCTIVE CONFORMAL PREDICTOR FOR SPARSE CODING CLASSIFIERS: APPLICATIONS TO IMAGE CLASSIFICATION. Available at: http://sigport.org/4512.
Kenneth E. Barner. (2019). "INDUCTIVE CONFORMAL PREDICTOR FOR SPARSE CODING CLASSIFIERS: APPLICATIONS TO IMAGE CLASSIFICATION." Web.
1. Kenneth E. Barner. INDUCTIVE CONFORMAL PREDICTOR FOR SPARSE CODING CLASSIFIERS: APPLICATIONS TO IMAGE CLASSIFICATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4512

TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION


We consider the problem of detecting whether a tensor signal having many missing entities lies within a given low dimensional Kronecker-Structured (KS) subspace. This is a matched subspace detection problem. Tensor matched subspace detection problem is more challenging because of the intertwined signal dimensions. We solve this problem by projecting the signal onto the KS subspace, which is a Kronecker product of different subspaces corresponding to each signal dimension. Under this framework, we define the KS subspaces and the orthogonal projection of the signal onto the KS subspace.

Paper Details

Authors:
Ishan Jindal, Matthew Nokleby
Submitted On:
12 May 2019 - 1:39pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Poster for the paper titled TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION

(25)

Subscribe

[1] Ishan Jindal, Matthew Nokleby, "TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4468. Accessed: Aug. 18, 2019.
@article{4468-19,
url = {http://sigport.org/4468},
author = {Ishan Jindal; Matthew Nokleby },
publisher = {IEEE SigPort},
title = {TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION},
year = {2019} }
TY - EJOUR
T1 - TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION
AU - Ishan Jindal; Matthew Nokleby
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4468
ER -
Ishan Jindal, Matthew Nokleby. (2019). TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION. IEEE SigPort. http://sigport.org/4468
Ishan Jindal, Matthew Nokleby, 2019. TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION. Available at: http://sigport.org/4468.
Ishan Jindal, Matthew Nokleby. (2019). "TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION." Web.
1. Ishan Jindal, Matthew Nokleby. TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4468

NEUROMORPHIC VISION SENSING FOR CNN-BASED ACTION RECOGNITION


Neuromorphic vision sensing (NVS) hardware is now gaining traction as a low-power/high-speed visual sensing technology that circumvents the limitations of conventional active pixel sensing (APS) cameras. While object detection and tracking models have been investigated in conjunction with NVS, there is currently little work on NVS for higher-level semantic tasks, such as action recognition.

Paper Details

Authors:
Aaron Chadha, Yin Bi, Alhabib Abbas, Yiannis Andreopoulos
Submitted On:
10 May 2019 - 9:31am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

NEUROMORPHIC VISION SENSING FOR CNN-BASED ACTION RECOGNITION.pdf

(36)

Subscribe

[1] Aaron Chadha, Yin Bi, Alhabib Abbas, Yiannis Andreopoulos, "NEUROMORPHIC VISION SENSING FOR CNN-BASED ACTION RECOGNITION ", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4328. Accessed: Aug. 18, 2019.
@article{4328-19,
url = {http://sigport.org/4328},
author = {Aaron Chadha; Yin Bi; Alhabib Abbas; Yiannis Andreopoulos },
publisher = {IEEE SigPort},
title = {NEUROMORPHIC VISION SENSING FOR CNN-BASED ACTION RECOGNITION },
year = {2019} }
TY - EJOUR
T1 - NEUROMORPHIC VISION SENSING FOR CNN-BASED ACTION RECOGNITION
AU - Aaron Chadha; Yin Bi; Alhabib Abbas; Yiannis Andreopoulos
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4328
ER -
Aaron Chadha, Yin Bi, Alhabib Abbas, Yiannis Andreopoulos. (2019). NEUROMORPHIC VISION SENSING FOR CNN-BASED ACTION RECOGNITION . IEEE SigPort. http://sigport.org/4328
Aaron Chadha, Yin Bi, Alhabib Abbas, Yiannis Andreopoulos, 2019. NEUROMORPHIC VISION SENSING FOR CNN-BASED ACTION RECOGNITION . Available at: http://sigport.org/4328.
Aaron Chadha, Yin Bi, Alhabib Abbas, Yiannis Andreopoulos. (2019). "NEUROMORPHIC VISION SENSING FOR CNN-BASED ACTION RECOGNITION ." Web.
1. Aaron Chadha, Yin Bi, Alhabib Abbas, Yiannis Andreopoulos. NEUROMORPHIC VISION SENSING FOR CNN-BASED ACTION RECOGNITION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4328

Feature Selection for Multi-labeled Variables via Dependency Maximization


Feature selection and reducing the dimensionality of data is an essential step in data analysis. In this work, we propose a new criterion for feature selection that is formulated as conditional information between features given the labeled variable. Instead of using the standard mutual information measure based on Kullback-Leibler divergence, we use our proposed criterion to filter out redundant features for the purpose of multiclass classification.

Paper Details

Authors:
Salimeh Yasaei Sekeh, Alfred O. Hero
Submitted On:
9 May 2019 - 9:36am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP2019-Salimeh-V2.pdf

(21)

Subscribe

[1] Salimeh Yasaei Sekeh, Alfred O. Hero, "Feature Selection for Multi-labeled Variables via Dependency Maximization", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4202. Accessed: Aug. 18, 2019.
@article{4202-19,
url = {http://sigport.org/4202},
author = {Salimeh Yasaei Sekeh; Alfred O. Hero },
publisher = {IEEE SigPort},
title = {Feature Selection for Multi-labeled Variables via Dependency Maximization},
year = {2019} }
TY - EJOUR
T1 - Feature Selection for Multi-labeled Variables via Dependency Maximization
AU - Salimeh Yasaei Sekeh; Alfred O. Hero
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4202
ER -
Salimeh Yasaei Sekeh, Alfred O. Hero. (2019). Feature Selection for Multi-labeled Variables via Dependency Maximization. IEEE SigPort. http://sigport.org/4202
Salimeh Yasaei Sekeh, Alfred O. Hero, 2019. Feature Selection for Multi-labeled Variables via Dependency Maximization. Available at: http://sigport.org/4202.
Salimeh Yasaei Sekeh, Alfred O. Hero. (2019). "Feature Selection for Multi-labeled Variables via Dependency Maximization." Web.
1. Salimeh Yasaei Sekeh, Alfred O. Hero. Feature Selection for Multi-labeled Variables via Dependency Maximization [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4202

AUTOMATIC RADAR-BASED GESTURE DETECTION VIA REGION-BASED DCNN


In this paper, a region-based deep convolutional neural network
(R-DCNN) is proposed to detect and classify gestures
measured by a frequency-modulated continuous wave radar
system. Micro-Doppler (μD) signatures of gestures are exploited,
and the resulting spectrograms are fed into a neural
network. We are the first to use the R-DCNN for radar-based
gesture recognition, such that multiple gestures could be automatically
detected and classified without manually clipping
the data streams according to each hand movement in advance.

Paper Details

Authors:
Yuliang Sun, Tai Fei, Shangyin Gao, Nils Pohl
Submitted On:
9 May 2019 - 7:54am
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

201904_ICASSP_Poster_SunYuliang.pdf

(29)

Subscribe

[1] Yuliang Sun, Tai Fei, Shangyin Gao, Nils Pohl, "AUTOMATIC RADAR-BASED GESTURE DETECTION VIA REGION-BASED DCNN ", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4195. Accessed: Aug. 18, 2019.
@article{4195-19,
url = {http://sigport.org/4195},
author = {Yuliang Sun; Tai Fei; Shangyin Gao; Nils Pohl },
publisher = {IEEE SigPort},
title = {AUTOMATIC RADAR-BASED GESTURE DETECTION VIA REGION-BASED DCNN },
year = {2019} }
TY - EJOUR
T1 - AUTOMATIC RADAR-BASED GESTURE DETECTION VIA REGION-BASED DCNN
AU - Yuliang Sun; Tai Fei; Shangyin Gao; Nils Pohl
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4195
ER -
Yuliang Sun, Tai Fei, Shangyin Gao, Nils Pohl. (2019). AUTOMATIC RADAR-BASED GESTURE DETECTION VIA REGION-BASED DCNN . IEEE SigPort. http://sigport.org/4195
Yuliang Sun, Tai Fei, Shangyin Gao, Nils Pohl, 2019. AUTOMATIC RADAR-BASED GESTURE DETECTION VIA REGION-BASED DCNN . Available at: http://sigport.org/4195.
Yuliang Sun, Tai Fei, Shangyin Gao, Nils Pohl. (2019). "AUTOMATIC RADAR-BASED GESTURE DETECTION VIA REGION-BASED DCNN ." Web.
1. Yuliang Sun, Tai Fei, Shangyin Gao, Nils Pohl. AUTOMATIC RADAR-BASED GESTURE DETECTION VIA REGION-BASED DCNN [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4195

Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection


In this paper, we adapt Recurrent Neural Networks with Stochastic Layers, which are the state-of-the-art for generating text, music and speech, to the problem of acoustic novelty detection. By integrating uncertainty into the hidden states, this type of network is able to learn the distribution of complex sequences. Because the learned distribution can be calculated explicitly in terms of probability, we can evaluate how likely an observation is then detect low-probability events as novel.

Paper Details

Authors:
Duong Nguyen , Oliver S. Kirsebom , Fábio Frazão , Ronan Fablet , Stan Matwin
Submitted On:
9 May 2019 - 6:26am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP2019_3073.pdf

(19)

Subscribe

[1] Duong Nguyen , Oliver S. Kirsebom , Fábio Frazão , Ronan Fablet , Stan Matwin, "Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4185. Accessed: Aug. 18, 2019.
@article{4185-19,
url = {http://sigport.org/4185},
author = {Duong Nguyen ; Oliver S. Kirsebom ; Fábio Frazão ; Ronan Fablet ; Stan Matwin },
publisher = {IEEE SigPort},
title = {Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection},
year = {2019} }
TY - EJOUR
T1 - Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection
AU - Duong Nguyen ; Oliver S. Kirsebom ; Fábio Frazão ; Ronan Fablet ; Stan Matwin
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4185
ER -
Duong Nguyen , Oliver S. Kirsebom , Fábio Frazão , Ronan Fablet , Stan Matwin. (2019). Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection. IEEE SigPort. http://sigport.org/4185
Duong Nguyen , Oliver S. Kirsebom , Fábio Frazão , Ronan Fablet , Stan Matwin, 2019. Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection. Available at: http://sigport.org/4185.
Duong Nguyen , Oliver S. Kirsebom , Fábio Frazão , Ronan Fablet , Stan Matwin. (2019). "Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection." Web.
1. Duong Nguyen , Oliver S. Kirsebom , Fábio Frazão , Ronan Fablet , Stan Matwin. Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4185

Learning Motion Disfluencies for Automatic Sign Language Segmentation


We introduce a novel technique for the automatic detection of word boundaries within continuous sentence expressions in Japanese Sign Language from three-dimensional body joint positions. First, the flow of signed sentence data within a temporal neighborhood is determined utilizing the spatial correlations between line segments of inter-joint pairs. Next, a frame-wise binary random forest classifier is trained to distinguish word and non-word frame content based on the extracted spatio-temporal features.

Paper Details

Authors:
Iva Farag
Submitted On:
9 May 2019 - 2:18am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Poster.pdf

(85)

Subscribe

[1] Iva Farag, "Learning Motion Disfluencies for Automatic Sign Language Segmentation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4154. Accessed: Aug. 18, 2019.
@article{4154-19,
url = {http://sigport.org/4154},
author = {Iva Farag },
publisher = {IEEE SigPort},
title = {Learning Motion Disfluencies for Automatic Sign Language Segmentation},
year = {2019} }
TY - EJOUR
T1 - Learning Motion Disfluencies for Automatic Sign Language Segmentation
AU - Iva Farag
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4154
ER -
Iva Farag. (2019). Learning Motion Disfluencies for Automatic Sign Language Segmentation. IEEE SigPort. http://sigport.org/4154
Iva Farag, 2019. Learning Motion Disfluencies for Automatic Sign Language Segmentation. Available at: http://sigport.org/4154.
Iva Farag. (2019). "Learning Motion Disfluencies for Automatic Sign Language Segmentation." Web.
1. Iva Farag. Learning Motion Disfluencies for Automatic Sign Language Segmentation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4154

A LARGE SCALE ANALYSIS OF LOGISTIC REGRESSION: ASYMPTOTIC PERFORMANCE AND NEW INSIGHTS

Paper Details

Authors:
Romain COUILLET
Submitted On:
8 May 2019 - 1:17pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster_ICASSP_LR_2.pdf

(16)

Subscribe

[1] Romain COUILLET, "A LARGE SCALE ANALYSIS OF LOGISTIC REGRESSION: ASYMPTOTIC PERFORMANCE AND NEW INSIGHTS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4124. Accessed: Aug. 18, 2019.
@article{4124-19,
url = {http://sigport.org/4124},
author = {Romain COUILLET },
publisher = {IEEE SigPort},
title = {A LARGE SCALE ANALYSIS OF LOGISTIC REGRESSION: ASYMPTOTIC PERFORMANCE AND NEW INSIGHTS},
year = {2019} }
TY - EJOUR
T1 - A LARGE SCALE ANALYSIS OF LOGISTIC REGRESSION: ASYMPTOTIC PERFORMANCE AND NEW INSIGHTS
AU - Romain COUILLET
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4124
ER -
Romain COUILLET. (2019). A LARGE SCALE ANALYSIS OF LOGISTIC REGRESSION: ASYMPTOTIC PERFORMANCE AND NEW INSIGHTS. IEEE SigPort. http://sigport.org/4124
Romain COUILLET, 2019. A LARGE SCALE ANALYSIS OF LOGISTIC REGRESSION: ASYMPTOTIC PERFORMANCE AND NEW INSIGHTS. Available at: http://sigport.org/4124.
Romain COUILLET. (2019). "A LARGE SCALE ANALYSIS OF LOGISTIC REGRESSION: ASYMPTOTIC PERFORMANCE AND NEW INSIGHTS." Web.
1. Romain COUILLET. A LARGE SCALE ANALYSIS OF LOGISTIC REGRESSION: ASYMPTOTIC PERFORMANCE AND NEW INSIGHTS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4124

Analysis Dictionary Learning: An Efficient and Discriminative Solution

Paper Details

Authors:
Submitted On:
8 May 2019 - 12:49pm
Short Link:
Type:
Event:
Paper Code:
Document Year:
Cite

Document Files

comprehensivecrimson_48x36.pdf

(15)

Subscribe

[1] , "Analysis Dictionary Learning: An Efficient and Discriminative Solution", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4123. Accessed: Aug. 18, 2019.
@article{4123-19,
url = {http://sigport.org/4123},
author = { },
publisher = {IEEE SigPort},
title = {Analysis Dictionary Learning: An Efficient and Discriminative Solution},
year = {2019} }
TY - EJOUR
T1 - Analysis Dictionary Learning: An Efficient and Discriminative Solution
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4123
ER -
. (2019). Analysis Dictionary Learning: An Efficient and Discriminative Solution. IEEE SigPort. http://sigport.org/4123
, 2019. Analysis Dictionary Learning: An Efficient and Discriminative Solution. Available at: http://sigport.org/4123.
. (2019). "Analysis Dictionary Learning: An Efficient and Discriminative Solution." Web.
1. . Analysis Dictionary Learning: An Efficient and Discriminative Solution [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4123

MATERIAL IDENTIFICATION USING RF SENSORS AND CONVOLUTIONAL NEURAL NETWORKS


Recent years have assisted a widespreading of Radio-Frequency-based tracking and mapping algorithms for a wide range of applications, ranging from environment surveillance to human-computer interface.

Paper Details

Authors:
Gianluca Agresti, Simone Milani
Submitted On:
8 May 2019 - 9:34am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster.pdf

(36)

Subscribe

[1] Gianluca Agresti, Simone Milani, "MATERIAL IDENTIFICATION USING RF SENSORS AND CONVOLUTIONAL NEURAL NETWORKS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4098. Accessed: Aug. 18, 2019.
@article{4098-19,
url = {http://sigport.org/4098},
author = {Gianluca Agresti; Simone Milani },
publisher = {IEEE SigPort},
title = {MATERIAL IDENTIFICATION USING RF SENSORS AND CONVOLUTIONAL NEURAL NETWORKS},
year = {2019} }
TY - EJOUR
T1 - MATERIAL IDENTIFICATION USING RF SENSORS AND CONVOLUTIONAL NEURAL NETWORKS
AU - Gianluca Agresti; Simone Milani
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4098
ER -
Gianluca Agresti, Simone Milani. (2019). MATERIAL IDENTIFICATION USING RF SENSORS AND CONVOLUTIONAL NEURAL NETWORKS. IEEE SigPort. http://sigport.org/4098
Gianluca Agresti, Simone Milani, 2019. MATERIAL IDENTIFICATION USING RF SENSORS AND CONVOLUTIONAL NEURAL NETWORKS. Available at: http://sigport.org/4098.
Gianluca Agresti, Simone Milani. (2019). "MATERIAL IDENTIFICATION USING RF SENSORS AND CONVOLUTIONAL NEURAL NETWORKS." Web.
1. Gianluca Agresti, Simone Milani. MATERIAL IDENTIFICATION USING RF SENSORS AND CONVOLUTIONAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4098

Pages