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Unlabeled sensing: Reconstruction algorithm and theoretical guarantees
2017
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Previous work on this topic has only considered the noiseless case and exhaustive search combinatorial algorithms. ...
Finally we provide simulation results to confirm the theoretical findings of the paper. ...
UNLABELED SENSING RECONSTRUCTION The focus of earlier papers on unlabeled sensing has been mostly on the uniqueness studies and not on the reconstruction algorithms [4] . ...
doi:10.1109/icassp.2017.7953021
dblp:conf/icassp/ElhamiSHV17
fatcat:n3rncycodbhcvecxodtx73vs5e
The Benefits of Diversity: Permutation Recovery in Unlabeled Sensing from Multiple Measurement Vectors
[article]
2020
arXiv
pre-print
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In "Unlabeled Sensing", one observes a set of linear measurements of an underlying signal with incomplete or missing information about their ordering, which can be modeled in terms of an unknown permutation ...
Numerical experiments based on the proposed computational scheme confirm the tightness of our theoretical analysis. ...
"Computable" refers to the availability of practical computational schemes that achieve the theoretical guarantees established in each work. ...
arXiv:1909.02496v2
fatcat:qqc4daw6hregne57pe6q4ntzgu
Signal Recovery From Unlabeled Samples
2018
IEEE Transactions on Signal Processing
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2017 pre-print arXiv:1701.08701v3 fatcat:mmovd3g6wvcslmbrdzbzmtczbeOther Versions
We analyze our proposed algorithm for different signal dimensions and number of measurements theoretically and investigate its performance empirically via simulations. ...
We consider a special case of unlabeled sensing referred to as Unlabeled Ordered Sampling (UOS) where the ordering of the measurements is preserved. ...
Fig. 1 : 1 Comparison between Compressed Sensing and Unlabeled Ordered Sensing.
Fig. 2: Unlabeled Ordered Sampling. ...
doi:10.1109/tsp.2017.2786276
fatcat:taxip3oifjae7n3qocurblx254
Online Positive and Unlabeled Learning
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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Therefore, this paper proposes a novel positive and unlabeled learning algorithm in an online training mode, which trains a classifier solely on the positive and unlabeled data arriving in a sequential ...
Theoretically, we show that the proposed online PU learning method achieves low regret even though it receives sequential positive and unlabeled data. ...
Acknowledgements Aurélien Garivier (ANR chaire SeqALO) and Tomáš Kocák acknowledge the support of the Project IDEXLYON of the University of Lyon, in the framework of the Programme Investissements d'Avenir ...
doi:10.24963/ijcai.2020/307
dblp:conf/ijcai/KocakG20
fatcat:pp4b2kbprnfq3og22fkpqpxl5i
Optimal Estimator for Unlabeled Linear Regression
2020
International Conference on Machine Learning
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However, the computation of unlabeled linear regression proves to be cumbersome and existing algorithms typically require considerable time, especially in the high dimensional regime. ...
Numerical experiments are also provided to corroborate the theoretical claims. ...
Acknowledgement We thank the anonymous reviewers and area chair of ICML 2020 for their constructive comments which have helped us improve the quality of the paper. ...
dblp:conf/icml/ZhangL20
fatcat:bfhtdnh3pbdx3lrrejng3bpsg4
Universum Prescription: Regularization using Unlabeled Data
[article]
2016
arXiv
pre-print
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In spite of its simplicity, universum prescription obtained competitive results in training deep convolutional networks for CIFAR-10, CIFAR-100, STL-10 and ImageNet datasets. ...
The effect of a regularization parameter -- probability of sampling from unlabeled data -- is also studied empirically. ...
Aditya Ramesh and Junbo Zhao helped cross-checking the proofs. ...
arXiv:1511.03719v7
fatcat:n6efhzi4bzhfzgkqgmoybrbuli
Trilateration using Unlabeled Path or Loop Lengths
[article]
2023
arXiv
pre-print
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Such a process has already been used for the simpler problem of reconstruction using unlabeled edge lengths. ...
We are interested in reconstructing 𝐩 given a set of edge, path and loop lengths. ...
Ioannis Gkioulekas and Todd Zickler received support from the DARPA REVEAL program under contract no. HR0011-16-C-0028. ...
arXiv:2012.14527v2
fatcat:fpsav5eqlvfcbnv6vlub3pvfya
How to trust unlabeled data? Instance Credibility Inference for Few-Shot Learning
[article]
2021
arXiv
pre-print
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Theoretically, under mild conditions of restricted eigenvalue, irrepresentability, and large error, our approach is guaranteed to collect all the correctly-predicted instances from the noisy pseudo-labeled ...
Typically, we repurpose the self-taught learning paradigm to predict pseudo-labels of unlabeled instances with an initial classifier trained from the few shot and then select the most confident ones to ...
Compared with those algorithms, our approach is much simpler and theoretically guaranteed. ...
arXiv:2007.08461v4
fatcat:fkzvzukqarcmje6kzwqbad2jb4
Universum Prescription: Regularization Using Unlabeled Data
2017
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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In spite of its simplicity, universum prescription obtained competitive results in training deep convolutional networks for CIFAR-10, CIFAR-100, STL-10 and ImageNet datasets. ...
The effect of a regularization parameter — probability of sampling from unlabeled data — is also studied empirically. ...
Aditya Ramesh and Junbo Zhao helped cross-checking the proofs. ...
doi:10.1609/aaai.v31i1.10768
fatcat:qbg62yftnvfk5ctkubl6zj7hni
Cadence: A Practical Time-series Partitioning Algorithm for Unlabeled IoT Sensor Streams
[article]
2022
arXiv
pre-print
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This paper introduces a sample-efficient, robust, time-series segmentation model and algorithm. ...
We demonstrate its applicability in a real-world IoT deployment for ambient-sensing based activity recognition. ...
However, the median heuristic does not guarantee maximum power and optimal performance of test statistics. ...
arXiv:2112.03360v2
fatcat:pxwqitjaqfbsjgsnv2mhg3mf64
Determining Generic Point Configurations From Unlabeled Path or Loop Lengths
[article]
2021
arXiv
pre-print
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2018 pre-print arXiv:1709.03936v2 fatcat:v3rht5l47zcgjcqv5t5kggjth4Other Versions
We also provide an algorithm, under a real computational model, for performing a reconstruction of 𝐩 from such unlabeled lengths. ...
Our main result is a condition on the set of paths or loops that is sufficient to guarantee such a unique determination. ...
Acknowledgements We would like to thank Dylan Thurston for numerous helpful conversations and suggestions throughout this project. ...
arXiv:1709.03936v3
fatcat:7tmkfusxzbcifffnkz2vj56wt4
Using unlabeled data in a sparse-coding framework for human activity recognition
2014
Pervasive and Mobile Computing
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Primarily we focus on transportation mode analysis task, a popular task in mobile-phone based sensing. ...
(i) It automatically derives a compact, sparse and meaningful feature representation of sensor data that does not rely on prior expert knowledge and generalizes extremely well across domain boundaries. ...
Hoyer for insightful discussions and comments on early versions of this work. The authors also acknowledge Mr. S. Hemminki for providing help and insights with the transportation mode data. S. ...
doi:10.1016/j.pmcj.2014.05.006
fatcat:svtf7unst5cpvpvd4mmtwcsfde
Homomorphic Sensing
[article]
2019
arXiv
pre-print
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On the algorithmic level we exhibit two dynamic programming based algorithms, which to the best of our knowledge are the first working solutions for the unlabeled sensing problem for small dimensions. ...
As a special case, we recover known conditions for unlabeled sensing, as well as new results and extensions. ...
Aldo Conca for first noting and sharing the phenomenon of Lemmas 2 and 3. We also thank Dr. Laurent Kneip for suggesting the case study of image registration under general affine transformations. ...
arXiv:1901.07852v3
fatcat:55himfjobnha3ienq3nowrjpyq
Class Prior Estimation with Biased Positives and Unlabeled Examples
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Finally, we derive an algorithm for estimating the class priors that relies on clustering to decompose the original problem into subproblems of unbiased positive-unlabeled learning. ...
posterior probabilities and the recovery of true classification performance. ...
The Theoretical Framework section gives rigorous identifiability results and the Estimation Algorithm derives an estimation procedure for bias correction. ...
doi:10.1609/aaai.v34i04.5848
fatcat:gu2dji4nprgtbpsmp5lljbntai
r-local sensing: Improved algorithm and applications
[article]
2022
arXiv
pre-print
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Applied to the r-local model, we show that the resulting algorithm is efficient. We validate the algorithm on synthetic and real datasets. ...
We propose a proximal alternating minimization algorithm for the general unlabeled sensing problem that provably converges to a first order stationary point. ...
PROPOSED APPROACH AND ALGORITHM In this section, we present a new algorithm for the r-local unlabeled sensing problem. ...
arXiv:2110.14034v3
fatcat:lbt3ye3gejf5zi24tmqa7rsg6e
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