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Masking Strategies for Image Manifolds
[article]
2016
arXiv
pre-print
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Masking Strategies for Image Manifolds Hamid Dadkhahi and Marco F. ...
Dadkhahi physical system or event governed by a few continuousvalued parameters. ...
arXiv:1606.04618v1
fatcat:bjahqbxd3bcnfcxsy2opvlos2i
Alternating Linear Bandits for Online Matrix-Factorization Recommendation
[article]
2018
arXiv
pre-print
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Dadkhahi
College of Information and Computer Sciences
University of Massachusetts Amherst ...
Alternating Linear Bandits
for Online Matrix-Factorization Recommendation
Hamid ...
arXiv:1810.09401v1
fatcat:h4mm75e4sngrjikkgxg33y4f5a
Learning Shallow Detection Cascades for Wearable Sensor-Based Mobile Health Applications
[article]
2016
arXiv
pre-print
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The field of mobile health aims to leverage recent advances in wearable on-body sensing technology and smart phone computing capabilities to develop systems that can monitor health states and deliver just-in-time adaptive interventions. However, existing work has largely focused on analyzing collected data in the off-line setting. In this paper, we propose a novel approach to learning shallow detection cascades developed explicitly for use in a real-time wearable-phone or wearable-phone-cloud
arXiv:1607.03730v1
fatcat:r25rcsbezvdhlpirqre7mbrlty
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... stems. We apply our approach to the problem of cigarette smoking detection from a combination of wrist-worn actigraphy data and respiration chest band data using two and three stage cascades.
Masking schemes for image manifolds
2014
2014 IEEE Workshop on Statistical Signal Processing (SSP)
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We consider the problem of selecting an optimal mask for an image manifold, i.e., choosing a subset of the dimensions of the image space that preserves the manifold structure present in the original data. Such masking implements a form of compressed sensing that reduces power consumption in emerging imaging sensor platforms. Our goal is for the manifold learned from masked images to resemble the manifold learned from full images as closely as possible. We show that the process of finding the
doi:10.1109/ssp.2014.6884623
dblp:conf/ssp/DadkhahiD14
fatcat:diztakrdtrbynbfvuujbone5ay
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... imal masking pattern can be cast as a binary integer program, which is computationally expensive but can be approximated by a fast greedy algorithm. Numerical experiments show that the manifolds learned from masked images resemble those learned from full images for modest mask sizes. Furthermore, our greedy algorithm performs similarly to the exhaustive search from integer programming at a fraction of the computational cost.
Design Of Higher Density Dual-Tree Discrete Wavelet Transform With Few Degrees Of Freedom
2008
Zenodo
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Publication in the conference proceedings of EUSIPCO, Lausanne, Switzerland, 2008
doi:10.5281/zenodo.40888
fatcat:2zl3sn3xxnhrrp2ujdg2zwbjce
Spectral Compressive Sensing with Polar Interpolation
[article]
2013
arXiv
pre-print
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Existing approaches to compressive sensing of frequency-sparse signals focuses on signal recovery rather than spectral estimation. Furthermore, the recovery performance is limited by the coherence of the required sparsity dictionaries and by the discretization of the frequency parameter space. In this paper, we introduce a greedy recovery algorithm that leverages a band-exclusion function and a polar interpolation function to address these two issues in spectral compressive sensing. Our
arXiv:1303.2799v3
fatcat:ap6g2t552jgsdlif3vj56fcmda
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... m is geared towards line spectral estimation from compressive measurements and outperforms most existing approaches in fidelity and tolerance to noise.
Combinatorial Black-Box Optimization with Expert Advice
[article]
2020
arXiv
pre-print
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We consider the problem of black-box function optimization over the boolean hypercube. Despite the vast literature on black-box function optimization over continuous domains, not much attention has been paid to learning models for optimization over combinatorial domains until recently. However, the computational complexity of the recently devised algorithms are prohibitive even for moderate numbers of variables; drawing one sample using the existing algorithms is more expensive than a function
arXiv:2006.03963v2
fatcat:gvrzyah5ibdijbjskzbxduqo6e
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... valuation for many black-box functions of interest. To address this problem, we propose a computationally efficient model learning algorithm based on multilinear polynomials and exponential weight updates. In the proposed algorithm, we alternate between simulated annealing with respect to the current polynomial representation and updating the weights using monomial experts' advice. Numerical experiments on various datasets in both unconstrained and sum-constrained boolean optimization indicate the competitive performance of the proposed algorithm, while improving the computational time up to several orders of magnitude compared to state-of-the-art algorithms in the literature.
Inverse polynomial reconstruction method in DCT domain
2012
EURASIP Journal on Advances in Signal Processing
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The discrete cosine transform (DCT) offers superior energy compaction properties for a large class of functions and has been employed as a standard tool in many signal and image processing applications. However, it suffers from spurious behavior in the vicinity of edge discontinuities in piecewise smooth signals. To leverage the sparse representation provided by the DCT, in this article, we derive a framework for the inverse polynomial reconstruction in the DCT expansion. It yields the
doi:10.1186/1687-6180-2012-133
fatcat:34fxhn4eznel5jcamd6tv4rvci
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... of a piecewise smooth signal in terms of polynomial coefficients, obtained from the DCT representation of the same signal. Taking advantage of this framework, we show that it is feasible to recover piecewise smooth signals from a relatively small number of DCT coefficients with high accuracy. Furthermore, automatic methods based on minimum description length principle and cross-validation are devised to select the polynomial orders, as a requirement of the inverse polynomial reconstruction method in practical applications. The developed framework can considerably enhance the performance of the DCT in sparse representation of piecewise smooth signals. Numerical results show that denoising and image approximation algorithms based on the proposed framework indicate significant improvements over wavelet counterparts for this class of signals.
Fourier Representations for Black-Box Optimization over Categorical Variables
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2202.03712v1 fatcat:ol4nxhmysjd37nqsjl3mr5g4ly
The sparsity parameter λ in exponential weight updates is set to 1 in all the experiments following the same choice made in (Dadkhahi et al. 2020) . ...
Surrogate Model Learning Sparse Online Regression: We adopt the learning algorithm of combinatorial optimization with expert advice (Dadkhahi et al. 2020) in the following way. ...
arXiv:2202.03712v2
fatcat:maadiycz7zd23c7oxvpvh3iywa
Image masking schemes for local manifold learning methods
2015
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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We consider the problem of selecting a subset of the dimensions of an image manifold that best preserves the underlying local structure in the original data. We have previously shown that masks which preserve the data neighborhood graph are well suited to global manifold learning algorithms. However, local manifold learning algorithms leverage a geometric structure beyond that captured by this neighborhood graph. In this paper, we present a mask selection algorithm that further preserves this
doi:10.1109/icassp.2015.7179077
dblp:conf/icassp/DadkhahiD15
fatcat:2j3cu5rlbvat7pluxt3ucq5wh4
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... ditional structure by designing an extended data neighborhood graph that connects all neighbors of each data point, forming local cliques. Numerical experiments show the improvements achieved by employing the extended graph in the mask selection process.
Spectral compressive sensing with polar interpolation
2013
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
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Existing approaches to compressive sensing of frequencysparse signals focuses on signal recovery rather than spectral estimation. Furthermore, the recovery performance is limited by the coherence of the required sparsity dictionaries and by the discretization of the frequency parameter space. In this paper, we introduce a greedy recovery algorithm that leverages a band-exclusion function and a polar interpolation function to address these two issues in spectral compressive sensing. Our
doi:10.1109/icassp.2013.6638862
dblp:conf/icassp/FyhnDD13
fatcat:xe6h3gm3qbhlnhbhc4ujef74aa
more »
... is geared towards line spectral estimation from compressive measurements and outperforms most existing approaches in fidelity and tolerance to noise.
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series
2017
IEEE Transactions on Image Processing
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2017 pre-print arXiv:1606.08282v2 fatcat:dghc6azs2jdhnhlnmqnrbqshleOther Versions
Dadkhahi high-dimensional ambient space despite possessing merely a few degrees of freedom. ...
doi:10.1109/tip.2017.2735189
pmid:28783635
fatcat:ttkwz4zmvzfqjgwyrkrstqu7bq
Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices
2017
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17
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Dadkhahi To begin, we assume we have access to a total of D + is defined as shown in Equation 2. ...
Dadkhahi Figure 5 . 5 In this figure, timing results are averaged over 1, 000 classifier evaluations. ...
doi:10.1145/3097983.3098169
pmid:29333328
pmcid:PMC5765542
dblp:conf/kdd/DadkhahiM17
fatcat:fnjpeeb575hsfa2ektwsozdyt4