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

Learning theory and algorithms (MLR-LEAR)

On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization


Active graph-based semi-supervised learning (AG-SSL) aims to select a small set of labeled examples and utilize their graph-based relation to other unlabeled examples to aid in machine learning tasks. It is also closely related to the sampling theory in graph signal processing. In this paper, we revisit the original formulation of graph-based SSL and prove the supermodularity of an AG-SSL objective function under a broad class of regularization functions parameterized by Stieltjes matrices.

Paper Details

Authors:
Pin-Yu Chen, Dennis Wei
Submitted On:
20 April 2018 - 12:31am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster

(8 downloads)

Keywords

Subscribe

[1] Pin-Yu Chen, Dennis Wei, "On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3067. Accessed: Apr. 23, 2018.
@article{3067-18,
url = {http://sigport.org/3067},
author = {Pin-Yu Chen; Dennis Wei },
publisher = {IEEE SigPort},
title = {On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization},
year = {2018} }
TY - EJOUR
T1 - On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization
AU - Pin-Yu Chen; Dennis Wei
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3067
ER -
Pin-Yu Chen, Dennis Wei. (2018). On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization. IEEE SigPort. http://sigport.org/3067
Pin-Yu Chen, Dennis Wei, 2018. On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization. Available at: http://sigport.org/3067.
Pin-Yu Chen, Dennis Wei. (2018). "On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization." Web.
1. Pin-Yu Chen, Dennis Wei. On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3067

THE CHORD GAP DIVERGENCE AND A GENERALIZATION OF THE BHATTACHARYYA DISTANCE

Paper Details

Authors:
Submitted On:
19 April 2018 - 10:46pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

Slides-ChordDivergence18April2018.pdf

(7 downloads)

Keywords

Subscribe

[1] , "THE CHORD GAP DIVERGENCE AND A GENERALIZATION OF THE BHATTACHARYYA DISTANCE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3059. Accessed: Apr. 23, 2018.
@article{3059-18,
url = {http://sigport.org/3059},
author = { },
publisher = {IEEE SigPort},
title = {THE CHORD GAP DIVERGENCE AND A GENERALIZATION OF THE BHATTACHARYYA DISTANCE},
year = {2018} }
TY - EJOUR
T1 - THE CHORD GAP DIVERGENCE AND A GENERALIZATION OF THE BHATTACHARYYA DISTANCE
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3059
ER -
. (2018). THE CHORD GAP DIVERGENCE AND A GENERALIZATION OF THE BHATTACHARYYA DISTANCE. IEEE SigPort. http://sigport.org/3059
, 2018. THE CHORD GAP DIVERGENCE AND A GENERALIZATION OF THE BHATTACHARYYA DISTANCE. Available at: http://sigport.org/3059.
. (2018). "THE CHORD GAP DIVERGENCE AND A GENERALIZATION OF THE BHATTACHARYYA DISTANCE." Web.
1. . THE CHORD GAP DIVERGENCE AND A GENERALIZATION OF THE BHATTACHARYYA DISTANCE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3059

PARALLEL VECTOR FIELD REGULARIZED NON-NEGATIVE MATRIX FACTORIZATION FOR IMAGE REPRESENTATION

Paper Details

Authors:
Submitted On:
19 April 2018 - 2:13pm
Short Link:
Type:
Event:
Paper Code:
Document Year:
Cite

Document Files

presentation_1.pdf

(7 downloads)

Keywords

Subscribe

[1] , "PARALLEL VECTOR FIELD REGULARIZED NON-NEGATIVE MATRIX FACTORIZATION FOR IMAGE REPRESENTATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2988. Accessed: Apr. 23, 2018.
@article{2988-18,
url = {http://sigport.org/2988},
author = { },
publisher = {IEEE SigPort},
title = {PARALLEL VECTOR FIELD REGULARIZED NON-NEGATIVE MATRIX FACTORIZATION FOR IMAGE REPRESENTATION},
year = {2018} }
TY - EJOUR
T1 - PARALLEL VECTOR FIELD REGULARIZED NON-NEGATIVE MATRIX FACTORIZATION FOR IMAGE REPRESENTATION
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2988
ER -
. (2018). PARALLEL VECTOR FIELD REGULARIZED NON-NEGATIVE MATRIX FACTORIZATION FOR IMAGE REPRESENTATION. IEEE SigPort. http://sigport.org/2988
, 2018. PARALLEL VECTOR FIELD REGULARIZED NON-NEGATIVE MATRIX FACTORIZATION FOR IMAGE REPRESENTATION. Available at: http://sigport.org/2988.
. (2018). "PARALLEL VECTOR FIELD REGULARIZED NON-NEGATIVE MATRIX FACTORIZATION FOR IMAGE REPRESENTATION." Web.
1. . PARALLEL VECTOR FIELD REGULARIZED NON-NEGATIVE MATRIX FACTORIZATION FOR IMAGE REPRESENTATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2988

A Greedy Pursuit Algorithm For Separating Signals From Nonlinear Compressive Observations


In this paper we study the unmixing problem which aims
to separate a set of structured signals from their superposition.
In this paper, we consider the scenario in which
the mixture is observed via nonlinear compressive measurements.
We present a fast, robust, greedy algorithm called
Unmixing Matching Pursuit (UnmixMP) to solve this problem.
We prove rigorously that the algorithm can recover the
constituents from their noisy nonlinear compressive measurements
with arbitrarily small error. We compare our algorithm

Paper Details

Authors:
Dung Tran, Akshay Rangamani, Sang (Peter) Chin, Trac D. Tran
Submitted On:
17 April 2018 - 1:05pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

A Greedy Pursuit Algorithm For Separating Signals From.pdf

(6 downloads)

Keywords

Subscribe

[1] Dung Tran, Akshay Rangamani, Sang (Peter) Chin, Trac D. Tran, "A Greedy Pursuit Algorithm For Separating Signals From Nonlinear Compressive Observations", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2939. Accessed: Apr. 23, 2018.
@article{2939-18,
url = {http://sigport.org/2939},
author = {Dung Tran; Akshay Rangamani; Sang (Peter) Chin; Trac D. Tran },
publisher = {IEEE SigPort},
title = {A Greedy Pursuit Algorithm For Separating Signals From Nonlinear Compressive Observations},
year = {2018} }
TY - EJOUR
T1 - A Greedy Pursuit Algorithm For Separating Signals From Nonlinear Compressive Observations
AU - Dung Tran; Akshay Rangamani; Sang (Peter) Chin; Trac D. Tran
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2939
ER -
Dung Tran, Akshay Rangamani, Sang (Peter) Chin, Trac D. Tran. (2018). A Greedy Pursuit Algorithm For Separating Signals From Nonlinear Compressive Observations. IEEE SigPort. http://sigport.org/2939
Dung Tran, Akshay Rangamani, Sang (Peter) Chin, Trac D. Tran, 2018. A Greedy Pursuit Algorithm For Separating Signals From Nonlinear Compressive Observations. Available at: http://sigport.org/2939.
Dung Tran, Akshay Rangamani, Sang (Peter) Chin, Trac D. Tran. (2018). "A Greedy Pursuit Algorithm For Separating Signals From Nonlinear Compressive Observations." Web.
1. Dung Tran, Akshay Rangamani, Sang (Peter) Chin, Trac D. Tran. A Greedy Pursuit Algorithm For Separating Signals From Nonlinear Compressive Observations [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2939

AN IMPROVED INITIALIZATION FOR LOW-RANK MATRIX COMPLETION BASED ON RANK-1 UPDATES


Given a data matrix with partially observed entries, the low-rank matrix completion problem is one of finding a matrix with the lowest rank that perfectly fits the given observations. While there exist convex relaxations for the low-rank completion problem, the underlying problem is inherently non-convex, and most algorithms (alternating projection, Riemannian optimization, etc.) heavily depend on the initialization. This paper proposes an improved initialization that relies on successive rank-1 updates.

Paper Details

Authors:
Ahmed Douik, Babak Hassibi
Submitted On:
19 April 2018 - 3:10pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Presentation_v2.pdf

(5 downloads)

Presentation Slides

(5 downloads)

Keywords

Subscribe

[1] Ahmed Douik, Babak Hassibi, "AN IMPROVED INITIALIZATION FOR LOW-RANK MATRIX COMPLETION BASED ON RANK-1 UPDATES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2921. Accessed: Apr. 23, 2018.
@article{2921-18,
url = {http://sigport.org/2921},
author = {Ahmed Douik; Babak Hassibi },
publisher = {IEEE SigPort},
title = {AN IMPROVED INITIALIZATION FOR LOW-RANK MATRIX COMPLETION BASED ON RANK-1 UPDATES},
year = {2018} }
TY - EJOUR
T1 - AN IMPROVED INITIALIZATION FOR LOW-RANK MATRIX COMPLETION BASED ON RANK-1 UPDATES
AU - Ahmed Douik; Babak Hassibi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2921
ER -
Ahmed Douik, Babak Hassibi. (2018). AN IMPROVED INITIALIZATION FOR LOW-RANK MATRIX COMPLETION BASED ON RANK-1 UPDATES. IEEE SigPort. http://sigport.org/2921
Ahmed Douik, Babak Hassibi, 2018. AN IMPROVED INITIALIZATION FOR LOW-RANK MATRIX COMPLETION BASED ON RANK-1 UPDATES. Available at: http://sigport.org/2921.
Ahmed Douik, Babak Hassibi. (2018). "AN IMPROVED INITIALIZATION FOR LOW-RANK MATRIX COMPLETION BASED ON RANK-1 UPDATES." Web.
1. Ahmed Douik, Babak Hassibi. AN IMPROVED INITIALIZATION FOR LOW-RANK MATRIX COMPLETION BASED ON RANK-1 UPDATES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2921

Improved Algorithms for Differentially private Orthogonal Tensor Decomposition


Tensor decompositions have applications in many areas including signal processing, machine learning, computer vision and neuroscience. In this paper, we propose two new differentially private algorithms for orthogonal decomposition of symmetric tensors from private or sensitive data; these arise in applications such as latent variable models. Differential privacy is a formal privacy framework that guarantees protections against adversarial inference.

Paper Details

Authors:
Submitted On:
13 April 2018 - 2:23pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Presentation slides

(6 downloads)

Keywords

Subscribe

[1] , "Improved Algorithms for Differentially private Orthogonal Tensor Decomposition", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2743. Accessed: Apr. 23, 2018.
@article{2743-18,
url = {http://sigport.org/2743},
author = { },
publisher = {IEEE SigPort},
title = {Improved Algorithms for Differentially private Orthogonal Tensor Decomposition},
year = {2018} }
TY - EJOUR
T1 - Improved Algorithms for Differentially private Orthogonal Tensor Decomposition
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2743
ER -
. (2018). Improved Algorithms for Differentially private Orthogonal Tensor Decomposition. IEEE SigPort. http://sigport.org/2743
, 2018. Improved Algorithms for Differentially private Orthogonal Tensor Decomposition. Available at: http://sigport.org/2743.
. (2018). "Improved Algorithms for Differentially private Orthogonal Tensor Decomposition." Web.
1. . Improved Algorithms for Differentially private Orthogonal Tensor Decomposition [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2743

Differentially private Distributed Principal Component Analysis


Differential privacy is a cryptographically-motivated formal privacy definition that is robust against strong adversaries. The principal component analysis (PCA) algorithm is frequently used in signal processing, machine learning, and statistics pipelines. In many scenarios, private or sensitive data is distributed across different sites: in this paper we propose a differentially private distributed PCA scheme to enable collaborative dimensionality reduction.

Paper Details

Authors:
Submitted On:
13 April 2018 - 2:20pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Presentation slides

(15 downloads)

Keywords

Subscribe

[1] , "Differentially private Distributed Principal Component Analysis", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2742. Accessed: Apr. 23, 2018.
@article{2742-18,
url = {http://sigport.org/2742},
author = { },
publisher = {IEEE SigPort},
title = {Differentially private Distributed Principal Component Analysis},
year = {2018} }
TY - EJOUR
T1 - Differentially private Distributed Principal Component Analysis
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2742
ER -
. (2018). Differentially private Distributed Principal Component Analysis. IEEE SigPort. http://sigport.org/2742
, 2018. Differentially private Distributed Principal Component Analysis. Available at: http://sigport.org/2742.
. (2018). "Differentially private Distributed Principal Component Analysis." Web.
1. . Differentially private Distributed Principal Component Analysis [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2742

Robust Matrix Completion via Alternating Projection

Paper Details

Authors:
X. Jiang, Z. Zhong, X. Liu and H. C. So
Submitted On:
13 April 2018 - 6:44am
Short Link:
Type:
Event:
Paper Code:
Document Year:
Cite

Document Files

Paper 4733: Robust Matrix Completion via Alternating Projection Paper Identifier: MLSP-P1.4

(9 downloads)

Keywords

Subscribe

[1] X. Jiang, Z. Zhong, X. Liu and H. C. So, "Robust Matrix Completion via Alternating Projection", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2686. Accessed: Apr. 23, 2018.
@article{2686-18,
url = {http://sigport.org/2686},
author = {X. Jiang; Z. Zhong; X. Liu and H. C. So },
publisher = {IEEE SigPort},
title = {Robust Matrix Completion via Alternating Projection},
year = {2018} }
TY - EJOUR
T1 - Robust Matrix Completion via Alternating Projection
AU - X. Jiang; Z. Zhong; X. Liu and H. C. So
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2686
ER -
X. Jiang, Z. Zhong, X. Liu and H. C. So. (2018). Robust Matrix Completion via Alternating Projection. IEEE SigPort. http://sigport.org/2686
X. Jiang, Z. Zhong, X. Liu and H. C. So, 2018. Robust Matrix Completion via Alternating Projection. Available at: http://sigport.org/2686.
X. Jiang, Z. Zhong, X. Liu and H. C. So. (2018). "Robust Matrix Completion via Alternating Projection." Web.
1. X. Jiang, Z. Zhong, X. Liu and H. C. So. Robust Matrix Completion via Alternating Projection [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2686

DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS


In this paper, we propose a differentially-private canonical correlation analysis algorithm. Canonical correlation analysis (CCA) is often used in clustering applications for multi-view data. CCA finds subspaces for each view such that projecting each of the views onto these subspaces simultaneously reduces the dimension and maximizes correlation. Differential-privacy is a framework for understanding the risk of inferring the data input to the algorithm based on the output.

Paper Details

Authors:
Hafiz Imtiaz, Anand D. Sarwate
Submitted On:
9 November 2017 - 1:13pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Poster

(76 downloads)

Keywords

Subscribe

[1] Hafiz Imtiaz, Anand D. Sarwate, "DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2273. Accessed: Apr. 23, 2018.
@article{2273-17,
url = {http://sigport.org/2273},
author = {Hafiz Imtiaz; Anand D. Sarwate },
publisher = {IEEE SigPort},
title = {DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS},
year = {2017} }
TY - EJOUR
T1 - DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS
AU - Hafiz Imtiaz; Anand D. Sarwate
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2273
ER -
Hafiz Imtiaz, Anand D. Sarwate. (2017). DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS. IEEE SigPort. http://sigport.org/2273
Hafiz Imtiaz, Anand D. Sarwate, 2017. DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS. Available at: http://sigport.org/2273.
Hafiz Imtiaz, Anand D. Sarwate. (2017). "DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS." Web.
1. Hafiz Imtiaz, Anand D. Sarwate. DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2273

ONLINE CONVOLUTIONAL DICTIONARY LEARNING


While a number of different algorithms have recently been proposed for convolutional dictionary learning, this remains an expensive problem. The single biggest impediment to learning from large training sets is the memory requirements, which grow at least linearly with the size of the training set since all existing methods are batch algorithms. The work reported here addresses this limitation by extending online dictionary learning ideas to the convolutional context.

Paper Details

Authors:
Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg, Wotao Yin
Submitted On:
19 September 2017 - 9:30pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

slides_online_cdl

(74 downloads)

Keywords

Subscribe

[1] Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg, Wotao Yin, "ONLINE CONVOLUTIONAL DICTIONARY LEARNING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2237. Accessed: Apr. 23, 2018.
@article{2237-17,
url = {http://sigport.org/2237},
author = {Jialin Liu; Cristina Garcia-Cardona; Brendt Wohlberg; Wotao Yin },
publisher = {IEEE SigPort},
title = {ONLINE CONVOLUTIONAL DICTIONARY LEARNING},
year = {2017} }
TY - EJOUR
T1 - ONLINE CONVOLUTIONAL DICTIONARY LEARNING
AU - Jialin Liu; Cristina Garcia-Cardona; Brendt Wohlberg; Wotao Yin
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2237
ER -
Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg, Wotao Yin. (2017). ONLINE CONVOLUTIONAL DICTIONARY LEARNING. IEEE SigPort. http://sigport.org/2237
Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg, Wotao Yin, 2017. ONLINE CONVOLUTIONAL DICTIONARY LEARNING. Available at: http://sigport.org/2237.
Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg, Wotao Yin. (2017). "ONLINE CONVOLUTIONAL DICTIONARY LEARNING." Web.
1. Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg, Wotao Yin. ONLINE CONVOLUTIONAL DICTIONARY LEARNING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2237

Pages