Paper improves dictionary learning by addressing local and global coherence issues.
problem Improving dictionary learning by addressing local and global coherence issues.
method The paper uses the ITKrM algorithm to prove contraction under relaxed conditions and proposes replacing bad dictionaries with carefully designed candidates.
result The adaptive version of ITKrM can recover a generating dictionary from randomly initialized dictionaries of various sizes and learn meaningful dictionaries on image data.
A new tree-based method for adaptive dictionary learning.
problem Sparse encoding and multiscale structure in data.
method Hierarchical clustering and binary partition tree for adaptive dictionary learning.
result Dictionary atoms are organized in a multiscale structure, leading to better reconstruction.
A new approach to kernel adaptive filters reduces sparsity for monotonic signals.
problem Kernel adaptive filters struggle with trivial monotonic signals, leading to inaccurate predictions and high computational complexity.
method Proposes a unit-norm Gaussian kernel and sparsification criterion to compare new observations against dictionary samples.
result The method achieves more accurate predictions and smaller dictionary size compared to standard KAF.
Paper presents a method for robust surface reconstruction from noisy gradients using adaptive dictionary learning.
problem Reconstructing surfaces from noisy photometric stereo normal vector maps.
method Adaptive dictionary learning to sparsely represent spatial patches of the surface, enforcing smoothness constraints.
result The method effectively learns the underlying surface structure and is robust to noise.
Detects financial fraud schemes in networks using graph structure learning.
problem Identifying financial fraud schemes in complex networks.
method Adapting dictionary learning to network topologies, imposing Laplacian structure on dictionaries.
result Proposed methods effectively represent graph structure information for anomaly detection.
In sparse signal representation, the choice of a dictionary often involves a tradeoff between two desirable properties -- the ability to adapt to specific signal data and a fast implementation of the dictionary. To sparsely represent signals residing on weighted graphs, an additional design challenge is to incorporate …
OnAIR reconstructs dynamic images from sparse measurements online.
problem Reconstructing dynamic images from limited or corrupted measurements.
method Online adaptive reconstruction using sparsity and low-rank models with dictionary learning.
result Memory-efficient online algorithms for sequential estimation of dictionary and images.
A new travel time tomography method uses adaptive dictionaries to model slowness variations.
problem Modeling and reconstructing slowness maps with varying scales and discontinuities.
method Local model (sparse patches) and global model (smooth constraints) integrated into a maximum a posteriori formulation.
result The LST approach effectively models both smooth and discontinuous slowness features.
New photometric stereo method using learned dictionaries for robustness.
problem Estimating object normals from varying lighting conditions.
method Adaptive dictionary learning for image preprocessing and direct regularization of normal vectors.
result State-of-the-art performance in noisy conditions.
We consider the problem of distributed dictionary learning, where a set of nodes is required to collectively learn a common dictionary from noisy measurements. This approach may be useful in several contexts including sensor networks. Diffusion cooperation schemes have been proposed to solve the distributed linear regr…
Paper tackles online multi-source domain adaptation using Gaussian mixtures and dictionary learning.
problem Adapting multiple, heterogeneous source domains to a target domain in a streaming fashion.
method Introduces a novel approach for online fitting of Gaussian Mixture Models based on Wasserstein geometry, combined with dataset dictionary learning.
result Demonstrates ability to adapt 'on the fly' to target domain data streams.
In this paper, we focus on online representation learning in non-stationary environments which may require continuous adaptation of model architecture. We propose a novel online dictionary-learning (sparse-coding) framework which incorporates the addition and deletion of hidden units (dictionary elements), and is inspi…
Sparsity-based approaches have been popular in many applications in image processing and imaging. Compressed sensing exploits the sparsity of images in a transform domain or dictionary to improve image recovery from undersampled measurements. In the context of inverse problems in dynamic imaging, recent research has de…
Paper tackles adapting multiple domains to a target domain using distillation and dictionary learning.
problem Adapting multiple heterogeneous labeled source domains to an unlabeled target domain.
method Combines Multi-Source Domain Adaptation and Dataset Distillation with Dataset Dictionary Learning.
result Achieves state-of-the-art adaptation performance even with minimal labeled data.
The paper tackles MSDA by learning dictionary atoms in Wasserstein space.
problem Mitigating data distribution shifts across multiple source domains to target domain.
method Dictionary learning and optimal transport in Wasserstein space; DaDiL algorithm for learning.
result Improved classification performance by 3.15%, 2.29%, and 7.71% in benchmarks.
Dictionary Learning has proven to be a powerful tool for many image processing tasks, where atoms are typically defined on small image patches. As a drawback, the dictionary only encodes basic structures. In addition, this approach treats patches of different locations in one single set, which means a loss of informati…
Ada-LISTA adapts neural solvers for varying models.
problem Adapting neural solvers for varying models.
method Ada-LISTA receives pairs of signals and dictionaries, learns a universal architecture, and solves sparse coding in linear rate.
result Ada-LISTA solves sparse coding in linear rate for varying models.
A decentralized approach for multi-source domain adaptation.
problem Transfer knowledge from multiple related domains to an unlabeled target domain.
method Federated Dataset Dictionary Learning (FedDaDiL) framework, eliminating central server, using Wasserstein barycenters.
result Our decentralized approach effectively adapts source domains to an unlabeled target domain.
Proposes a new method for kernel density estimation using stagewise minimization and a simple dictionary.
problem Kernel density estimation with data-adaptive weighting parameters and sparse representation.
method Stagewise minimization algorithm based on U-divergence and a simple dictionary. result Develops non-asymptotic error bound for the proposed estimator.
This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate s…
Sparse coding in learned dictionaries has been established as a successful approach for signal denoising, source separation and solving inverse problems in general. A dictionary learning method adapts an initial dictionary to a particular signal class by iteratively computing an approximate factorization of a training …
This paper proposes a subspace decomposition method based on an over-complete dictionary in sparse representation, called "Sparse Signal Subspace Decomposition" (or 3SD) method. This method makes use of a novel criterion based on the occurrence frequency of atoms of the dictionary over the data set. This criterion, wel…
The kernel least-mean-square (KLMS) algorithm is an appealing tool for online identification of nonlinear systems due to its simplicity and robustness. In addition to choosing a reproducing kernel and setting filter parameters, designing a KLMS adaptive filter requires to select a so-called dictionary in order to get a…
Many techniques in computer vision, machine learning, and statistics rely on the fact that a signal of interest admits a sparse representation over some dictionary. Dictionaries are either available analytically, or can be learned from a suitable training set. While analytic dictionaries permit to capture the global st…
Predicts COVID-19 spread using dictionary learning and online NMF.
problem Limited daily case data for accurate prediction.
method Joint dictionary learning and online NMF for short evolution instances.
result Learned dictionary patterns improve predictions over time.
A probabilistic framework optimizes kernel adaptive filters for better performance.
problem Improving the performance of kernel adaptive filters.
method Formulating a probabilistic model with prior distributions over weights, dictionaries, and kernel parameters. Using gradient-based maximum-a-posteriori optimisation and Markov chain Monte Carlo methods.
result The framework outperformed standard KAFs in terms of mean square error and dictionary sparsity.
Dictionary learning is the problem of estimating the collection of atomic elements that provide a sparse representation of measured/collected signals or data. This paper finds fundamental limits on the sample complexity of estimating dictionaries for tensor data by proving a lower bound on the minimax risk. This lower …
The paper proposes a method to learn discriminative multilevel dictionaries for supervised image classification.
problem Improving sparse representation for supervised image classification.
method Learning structured multilevel dictionaries with discriminative constraints for each class, using reconstruction errors of image patches.
result Competitive results compared to state-of-the-art methods on texture image classification.
DirNet compresses RNNs for mobile devices with minimal accuracy loss.
problem High computational and memory demands of RNNs on mobile devices.
method Dynamic dictionary learning for adaptive sparsity and compression rate.
result Significant accuracy improvement with eight times model size reduction.
A-DLISTA and VLISTA learn dictionaries and sparse representations under varying sensing matrices.
problem Learning dictionaries and sparse representations under varying sensing matrices.
method Augmented Dictionary Learning ISTA (A-DLISTA) and Variational Learning ISTA (VLISTA).
result VLISTA provides a probabilistic way to jointly learn the dictionary distribution and the reconstruction algorithm.
Proposes a new dictionary learning method for high-dimensional graph signals.
problem Challenges of traditional sparse representation methods in high-dimensional graph signals.
method Integrates graph topology implicitly through sparse combinations of graph-wavelet functions and explicitly through graph constraints.
result Demonstrates effectiveness in high-dimensional graph signal processing.
Sparse coding, which is the decomposition of a vector using only a few basis elements, is widely used in machine learning and image processing. The basis set, also called dictionary, is learned to adapt to specific data. This approach has proven to be very effective in many image processing tasks. Traditionally, the di…
A multi-way factor analysis model is introduced for tensor-variate data of any order. Each data item is represented as a (sparse) sum of Kruskal decompositions, a Kruskal-factor analysis (KFA). KFA is nonparametric and can infer both the tensor-rank of each dictionary atom and the number of dictionary atoms. The model …
New algorithm reduces dynamic regret by adapting to comparator complexity.
problem Nonstationary sequential decision making with unbounded domains.
method Sparse coding framework to adapt to comparator complexity.
result Improves dynamic regret bounds by adapting to comparator energy and sparsity.
Recent breakthrough results in compressed sensing (CS) have established that many high dimensional objects can be accurately recovered from a relatively small number of non- adaptive linear projection observations, provided that the objects possess a sparse representation in some basis. Subsequent efforts have shown th…
A new image interpolation model using sparse representation and nonlocal linear regression.
problem Image interpolation without blurring and noise.
method Sparse representation, nonlocal self-similarity, nonlocal linear regression, adaptive sub-dictionary learning, weighted encoding.
result Our method outperforms state-of-the-art methods in quantitative measures and visual quality.
We consider the problem of efficiently approximating and encoding high-dimensional data sampled from a probability distribution ρ in RD, that is nearly supported on a d-dimensional set M - for example supported on a d-dimensional Riemannian manifold. Geometric Multi-Resolution Analysis (GM…
Adaptive filtering algorithms operating in reproducing kernel Hilbert spaces have demonstrated superiority over their linear counterpart for nonlinear system identification. Unfortunately, an undesirable characteristic of these methods is that the order of the filters grows linearly with the number of input data. This …
Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience and signal processing. For signals such as natural images that admit such sparse representations, it is now well established that these models are well suited t…
A new CNN architecture tackles domain shifts with a dictionary approach.
problem Handling domain shifts in deep learning models.
method Decompose CNN layers into domain-specific and shared parts using a dictionary of atoms.
result The approach promotes shared semantics across domains with minimal additional parameters.
Study monitors wind turbine drivetrain bearings using dictionary learning from vibration data.
problem Early detection of faults in wind turbine drivetrain bearings with minimal false positives.
method Unsupervised dictionary learning from 46 months of vibration data.
result Abnormal dictionary adaptation signals faults 6-12 months before bearing or gearbox replacement.
This paper studies CSDL's reconstruction risk and finds it consistent in the ultra-sparse setting.
problem The statistical properties of convolutional sparse dictionary learning (CSDL).
method Identifies the minimax convergence rate of CSDL in terms of reconstruction risk, upper bounds the risk of an established CSDL estimator, and proves a matching lower bound.
result Consistency in reconstruction risk is possible precisely in the ultra-sparse setting.
A distributed algorithm learns patterns in large images and signals.
problem High-dimensional optimization in large images and signals.
method Distributed asynchronous algorithm with locally greedy coordinate descent.
result Patterns can be learned on large scales images from the Hubble Space Telescope.
Real-time spell checker adapts to new languages.
problem No real-time, language-adaptable spell checkers for non-English languages.
method Used Wikipedia and subtitles data to generate dictionaries, created noisy channel datasets, compared with industry tools.
result System performs well across 24 languages, outperforming existing tools.
Optimizing the acquisition matrix is useful for compressed sensing of signals that are sparse in overcomplete dictionaries, because the acquisition matrix can be adapted to the particular correlations of the dictionary atoms. In this paper a novel formulation of the optimization problem is proposed, in the form of a ra…
Two binary matrix factorization methods using dictionary learning are proposed.
problem Efficiently factorizing binary matrices for various applications.
method Binary adaptation of dictionary learning for binary matrices, focusing on speed and scalability.
result Effective factorizations of various data types produced.
Nonparametric estimation of the conditional distribution of a response given high-dimensional features is a challenging problem. It is important to allow not only the mean but also the variance and shape of the response density to change flexibly with features, which are massive-dimensional. We propose a multiscale dic…
This paper extends the recently proposed and theoretically justified iterative thresholding and K residual means algorithm ITKrM to learning dicionaries from incomplete/masked training data (ITKrMM). It further adapts the algorithm to the presence of a low rank component in the data and provides a strategy for recove…