A new ZSL algorithm uses shared sparse representations for unseen classes.
problem Classifying images from unseen classes using only semantic information.
method Coupled dictionary learning to represent visual and semantic features in an intermediate space.
result The proposed method outperforms state-of-the-art ZSL algorithms on benchmark datasets.
Lifelong learning improves model performance and zero-shot transfer using task descriptions.
problem Efficiently transferring knowledge between tasks in lifelong learning.
method Coupled dictionary learning using high-level task descriptions.
result Improves model performance and enables zero-shot learning.
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.
Deep learning reduces memory and computation for MRF recovery.
problem Heavy storage and computation requirements of dictionary-matching in MRF recovery.
method MRF-Net with dimensionality reduction and manifold clustering.
result MRF-Net saves over 60 times memory and computation.
Novel approach for estimating joint probability densities using tensor decompositions and dictionaries.
problem Estimating joint probability densities of mixed discrete and continuous variables.
method Low-rank tensor decomposition combined with dictionary learning.
result Better classification and lower error rates compared to existing methods.
DeepCAM learns convolutional dictionaries for image processing.
problem Processing high-dimensional signals like images efficiently.
method Introduces a Deep Convolutional Analysis Dictionary Model (DeepCAM) using convolutional dictionaries.
result DeepCAM achieves performance comparable to other methods on single image super-resolution.
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.
This work learns sparse tensor representations using mixtures of separable dictionaries.
problem Learning sparse representations of tensor data with structured models.
method Proposes and explores learning a mixture of separable dictionaries with sufficient conditions for local identifiability.
result Developed computational algorithms for batch and online learning.
NOODL solves dictionary and coefficient recovery for online learning.
problem Non-convex optimization challenges in dictionary learning.
method NOODL: Neurally plausible alternating Optimization-based Online Dictionary Learning.
result Exact recovery of dictionary and coefficients at geometric rate.
Bayesian method improves dictionary learning for complex problems.
problem Efficiently identifying relevant dictionary entries for complex inverse problems.
method Bayesian group sparsity coding and deflation steps to compress and identify relevant subdictionaries.
result Significant computational complexity reduction and improved glitch detection in LIGO experiment.
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…
Framework integrates brain connectivity data for clinical predictions.
problem Predicting clinical outcomes from brain connectivity data.
method Structurally-regularized Dynamic Dictionary Learning (sr-DDL) and LSTM-ANN block.
result Framework outperforms state-of-the-art approaches in clinical outcome prediction.
Paper proposes robust dictionary learning using concave losses.
problem Sensitivity to outliers in traditional dictionary learning methods.
method Generic framework based on concave losses, with results on composition of concave functions.
result Method better detects outliers and generates better dictionaries.
Sparse representations using learned dictionaries are being increasingly used with success in several data processing and machine learning applications. The availability of abundant training data necessitates the development of efficient, robust and provably good dictionary learning algorithms. Algorithmic stability an…
A parallel algorithm learns efficient Kronecker product dictionaries.
problem Sparse representation of 2D signals like images and hyperspectral data.
method Highly parallelizable algorithm for learning separable dictionaries.
result Competitive sparse representations at lower computational cost.
The paper tackles dictionary learning with almost sure error constraints.
problem Achieving desirable features in data representation with almost sure error constraints.
method Imposes almost sure recovery constraints and reformulates the problem as a convex-concave min-max problem, solved using gradient descent-ascent.
result Demonstrates the effectiveness of the proposed method in achieving almost sure error constraints in dictionary learning.
The paper provides guarantees for an alternating minimization algorithm in dictionary learning.
problem Dictionary learning problem of factorizing samples into a basis and sparse vectors.
method Alternating minimization procedure switching between ℓ1 minimization and gradient descent. result Local convergence guarantees for the alternating minimization algorithm under a new matrix infinity norm condition.
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 …
We study the Dictionary Learning (aka Sparse Coding) problem of obtaining a sparse representation of data points, by learning \emph{dictionary vectors} upon which the data points can be written as sparse linear combinations. We view this problem from a geometry perspective as the spanning set of a subspace arrangement,…
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 …
Study shows unique sharp local minimum in ℓ1-minimization for dictionary learning.
problem Global recovery of a dictionary from random linear combinations of atoms.
method Norm condition, explicit bound, perturbation-based test, Block Coordinate Descent algorithm.
result Reference dictionary is the unique sharp local minimum of the ℓ1 objective function. We present a two-stage approach for learning dictionaries for object classification tasks based on the principle of information maximization. The proposed method seeks a dictionary that is compact, discriminative, and generative. In the first stage, dictionary atoms are selected from an initial dictionary by maximizing…
Two algorithms converge to dictionary learning with geometric rate for non-uniform data.
problem Dictionary learning for non-uniform data models.
method Derivation of convergence conditions for MOD and ODL.
result Both algorithms converge to the generating dictionary with geometric rate under certain conditions.
In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time. This requires solving a simple (shallow) dictionary learning problem, the solution to this is well known. We apply the proposed technique on some benchmark deep…
Dictionary learning is a cutting-edge area in imaging processing, that has recently led to state-of-the-art results in many signal processing tasks. The idea is to conduct a linear decomposition of a signal using a few atoms of a learned and usually over-completed dictionary instead of a pre-defined basis. Determining …
DeepAM optimizes deep neural networks for image super-resolution.
problem Image super-resolution using deep neural networks.
method L-layer analysis dictionary model with IPAD and CAD.
result DeepAM outperforms deep neural networks with back-propagation.
New algorithm reduces dictionary learning complexity.
problem Efficiently learning dictionaries from high-dimensional data.
method IcTKM algorithm using dimensionality reduction and fast Fourier transform.
result Locally recovers dictionary with high probability.
We consider the problem of sparse coding, where each sample consists of a sparse linear combination of a set of dictionary atoms, and the task is to learn both the dictionary elements and the mixing coefficients. Alternating minimization is a popular heuristic for sparse coding, where the dictionary and the coefficient…
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.
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…
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…
CRsAE auto-encoder recovers convolutional dictionary from noisy signals.
problem Recovering a convolutional dictionary from noisy signals.
method Constrained recurrent sparse auto-encoder (CRsAE) architecture.
result CRsAE successfully recovers the underlying dictionary in the presence of noise.
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.
PUDLE method analyzes and improves unrolled sparse coding networks for dictionary learning.
problem Dictionary learning problem, representing data as a combination of few atoms.
method PUDLE method addresses challenges in unrolled sparse coding networks through theoretical analysis and practical strategies.
result PUDLE method provides conditions for recovering and preserving the support of the latent code, and resolves bias and instability issues.
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.
This work combines deep learning and sparse coding for CT image reconstruction.
problem Improving image quality in low-dose CT scans.
method Sparse signal representation using learned dictionaries, inspired by variational autoencoders and deep learning techniques.
result Regularization with learned dictionaries achieves competitive performance in CT reconstruction.
Structured sparse coding and the related structured dictionary learning problems are novel research areas in machine learning. In this paper we present a new application of structured dictionary learning for collaborative filtering based recommender systems. Our extensive numerical experiments demonstrate that the pres…
MSBDL learns multimodal dictionaries with flexibility and superior performance.
problem Learning dictionaries for datasets from multiple data sources.
method Multimodal sparse Bayesian dictionary learning (MSBDL) with joint sparsity constraint.
result MSBDL outperforms existing methods on synthetic and real datasets.
Novel algorithm learns sparse signal representations over topological spaces.
problem Sparse representation of signals over combinatorial topological spaces.
method Leveraging Hodge theory, the paper embeds topology into a dictionary structure via concatenated sub-dictionaries, each as a polynomial of Hodge Laplacians, and optimizes the dictionary coefficients and sparse signal representation via iterative alternating algorithms.
result Efficiently learned sparse representations and underlying relational structure of topological signals.
Proposes DCADL for efficient image classification with reduced complexity.
problem Efficiency and discriminative capability in DL methods for image classification.
method Jointly learns a convolutional analysis dictionary and a universal classifier, reducing time complexity.
result Achieves competitive accuracy with reduced computational cost.
We consider the problem of dictionary learning under the assumption that the observed signals can be represented as sparse linear combinations of the columns of a single large dictionary matrix. In particular, we analyze the minimax risk of the dictionary learning problem which governs the mean squared error (MSE) perf…
Probabilistic model for weakly supervised analysis dictionary learning.
problem Discriminative analysis dictionary learning under weak supervision.
method Probabilistic modeling with EM algorithm and graph reformulation.
result Improved classification performance compared to synthesis dictionary learning.
PerCDL learns personalized dictionaries for physiological signals combining global and local structures.
problem Representing datasets with both global and local structures in human physiological signals.
method Personalized Convolutional Dictionary Learning (PerCDL) that combines a global and personalized local dictionary.
result PerCDL effectively learns interpretable representations for human locomotion data.
New algorithms learn sparse dictionaries from incomplete data.
problem Learning dictionaries from incomplete data.
method Iterative descent algorithm with initialization using extra samples.
result Provable polynomial-time algorithms for dictionary learning from incomplete data.
We develop methods to learn dictionaries invariant under group symmetries, useful in cryo-EM and tracking.
problem Learning dictionaries invariant under group symmetries.
method Representation theory, non-abelian Fourier analysis, matrix orbitopes, alternating minimization.
result Effective dictionary learning for SO(3) symmetries with guarantees.
Subgradient descent learns orthogonal dictionaries efficiently.
problem Sparse coding and dictionary learning for data representation.
method Subgradient descent algorithm with random initialization.
result Subgradient descent can recover orthogonal dictionaries under mild assumptions.
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.
STARK learns structured dictionaries for tensor data.
problem Representing multidimensional data with structured dictionaries.
method Solves a convex relaxation of a nonconvex rank-1 tensor recovery problem.
result Empirical results show promising performance for tensors of any order.