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 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…
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.
The paper proposes a new dictionary learning method for faster and more accurate image classification.
problem Efficient and accurate image classification with compact dictionaries.
method Cross-label suppression and group regularization to learn a discriminative dictionary.
result The proposed method achieves better classification accuracy and computational efficiency compared to existing methods.
Agents learn shared dictionary elements and parameters in a decentralized online setting.
problem Discriminative dictionary learning in a distributed online setting.
method Formulated as a distributed stochastic program, solved using a block variant of the Arrow-Hurwicz saddle point algorithm with Lagrange multipliers.
result Decisions asymptotically achieve a first-order stationarity condition on average.
New method improves signal classification accuracy.
problem Traditional dictionary learning struggles with signal classification accuracy.
method Incorporates discriminative information into sparse-inducing models.
result Significantly outperforms state-of-the-art methods in multi-class classification.
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.
A new algorithm improves kernel-based sparse coding for better data representation.
problem Lack of consistency between training and test optimization frameworks in K-SRC.
method Confident K-SRC (CKSC) with novel discriminative terms and supervised dictionary learning.
result Improves discriminative performance and recall phase in classification.
A new deep learning tool learns multi-level dictionaries greedily.
problem Improving deep learning performance on benchmark datasets.
method Greedy learning of multi-level dictionaries, solving shallow dictionary learning problems sequentially.
result Our method outperforms other deep learning tools and state-of-the-art supervised dictionary learning methods.
Dictionary learning algorithms have been successfully used for both reconstructive and discriminative tasks, where an input signal is represented with a sparse linear combination of dictionary atoms. While these methods are mostly developed for single-modality scenarios, recent studies have demonstrated the advantages …
A multiple instance dictionary learning method using functions of multiple instances (DL-FUMI) is proposed to address target detection and two-class classification problems with inaccurate training labels. Given inaccurate training labels, DL-FUMI learns a set of target dictionary atoms that describe the most distincti…
Dictionary learning algorithms have been successfully used in both reconstructive and discriminative tasks, where the input signal is represented by a linear combination of a few dictionary atoms. While these methods are usually developed under ℓ1 sparsity constrain (prior) in the input domain, recent studies hav…
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.
Sparsity-based representations have recently led to notable results in various visual recognition tasks. In a separate line of research, Riemannian manifolds have been shown useful for dealing with features and models that do not lie in Euclidean spaces. With the aim of building a bridge between the two realms, we addr…
A method for MRI brain tumor segmentation using feature vectors and kernel dictionary learning.
problem Segmenting brain tumor regions in MRI images.
method Extracting feature vectors, training kernel dictionaries, and selecting informative feature vectors.
result The method outperforms other methods in segmentation accuracy and reduces training time.
Paper presents J-RFDL for robust DL in compressed space, improving data representation robustness and accuracy.
problem Improving data representation robustness and accuracy in the presence of noise and outliers.
method Joint Robust Factorization and Projective Dictionary Learning (J-RFDL) in a factorized compressed space.
result Delivers superior performance in data representation and classification over state-of-the-art methods.
Discriminative model improves bilingual lexicon induction.
problem Inducing accurate bilingual lexicons between languages.
method Combines bipartite matching and representation-based approaches with an efficient Viterbi EM algorithm.
result The prior improves the induced bilingual lexicons.
Paper studies SDL for better document and medical classification.
problem Imbalanced document classification and pneumonia detection.
method Developed novel convex and nonconvex algorithms for SDL.
result SDL improves classification accuracy when feature and label spaces differ.
New graph PCA and dictionary learning methods detect cyber intrusions.
problem Detecting anomalous connectivity patterns in graphs.
method Multi-centrality graph PCA and dictionary learning.
result Effective detection of anomalous connectivity patterns and attack classification.
Enhances water disaggregation for parallel appliances using shape features and Bayesian Discriminative Sparse Coding.
problem Accurately discriminate and disaggregate water consumption patterns from parallel appliances.
method Bayesian Discriminative Sparse Coding (BDSC-LP) with Laplace Prior, shape features, Gibbs sampling.
result Extensive experiments validate the effectiveness of the proposed model.
CDPL-Net integrates CNN and DL for better image representation.
problem Improving image representation learning by combining CNN and DL.
method CDPL-Net architecture combining CNN and DPL layers, using l1-norm for sparse representation, and efficient stochastic gradient descent.
result Enhanced performance compared to state-of-the-art methods.
Proposes a new deep neural network training method using dictionary learning.
problem Training deep neural networks efficiently and effectively.
method Uses dictionary learning as the basic building block, stacking layers with features from shallower layers as inputs for deeper layers.
result Outperforms existing state-of-the-art techniques in benchmark problems and real-world applications like age and gender classification.
Recent advances suggest that a wide range of computer vision problems can be addressed more appropriately by considering non-Euclidean geometry. This paper tackles the problem of sparse coding and dictionary learning in the space of symmetric positive definite matrices, which form a Riemannian manifold. With the aid of…
Paper builds a Persian wordnet using supervised learning.
problem Creating an accurate Persian wordnet.
method Used a Persian corpus and bi-lingual dictionary to generate initial links. Trained a classification system on a set of correct instances to discriminate correct from incorrect links.
result Achieved state-of-the-art results with a precision of 91.18%.
Simplicial learning improves classification by generating compact sparse representations.
problem Difficulty in distinguishing classes on the same subspace.
method Evolutionary simplicial learning approach to sparse representations.
result Evolutionary simplicial learning outperforms other methods in multi-class classification.
Nonnegative matrix factorization (NMF) with group sparsity constraints is formulated as a probabilistic graphical model and, assuming some observed data have been generated by the model, a feasible variational Bayesian algorithm is derived for learning model parameters. When used in a supervised learning scenario, NMF …
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.
Deep model generates and analyzes images using hierarchical convolutional learning.
problem Representation and analysis of images.
method Hierarchical convolutional dictionary-learning framework with stochastic unpooling, Bayesian support vector machine, and deep deconvolutional inference.
result Excellent results on benchmark datasets, competitive with convolutional neural networks.
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.
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.
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…
Study on classification and representation of multidimensional signals using Kronecker-structured models.
problem Performance limits and algorithms for classification and representation of multidimensional signals.
method Analysis of diversity order and classification capacity, development of K-SLD2 algorithm for fast Kronecker-structured learning.
result Agreement between diversity order analysis and empirical classification performance of K-S models.
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.
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…
Random dictionaries help solve complex inverse problems without strict assumptions.
problem Solving ill-posed linear inverse problems with overcomplete dictionaries.
method Apply random dictionaries to regression problems and study their performance.
result Random dictionaries can solve ill-posed linear inverse problems without stringent compatibility conditions.
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.
A new method learns a sparse dictionary and optimizes its size for better image processing.
problem Choosing the right size of the dictionary for optimal performance.
method Employed a novel regularization method (GSCAD) combined with ADMM for simultaneous sparse dictionary learning and size selection.
result The method improves image denoising performance compared to existing approaches.
Efficient algorithm selects atoms from dictionaries with complex sparsity constraints.
problem Dictionary selection with complex sparsity constraints.
method Novel efficient greedy algorithm for dictionary selection.
result Outperforms known methods in faster running time and competitive performance.
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.
Paper provides conditions for local recovery of tensor data's Kronecker-structured dictionaries.
problem Local recovery of Kronecker-structured dictionaries for tensor data.
method Derives sufficient conditions for local recovery of coordinate dictionaries.
result Sufficient conditions guarantee recovery of individual coordinate dictionaries up to specified error.
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 …
A new tensor decomposition method using a dictionary for better interpretability.
problem Ensuring interpretability in tensor decomposition models.
method Dictionary-based tensor canonical polyadic decomposition with sparse coding.
result Improves parameter identifiability and estimation accuracy in tensor decomposition.
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. A dictionary connects symplectic to contact geometry, with applications to complex and G-structures.
problem Formalizing the relationship between symplectic and contact geometry.
method Developing a Symplectic-to-Contact Dictionary.
result The dictionary can be applied to complex and G-structures, revealing new geometries.
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.
Researchers find optimal dictionaries for minimizing average squared coefficients in random vector representations.
problem Finding optimal dictionaries for minimizing the average squared coefficients in random vector representations.
method Using rank-1 decompositions and majorization theory, the study provides a complete characterization of optimal dictionaries.
result Complete characterization of ℓ2-optimal dictionaries with polynomial time algorithms. Optimal dictionaries minimize the average squared error in representing random vectors.
problem Finding optimal dictionaries for minimizing ℓ2-norm of coefficients in random vector representations. method Using rank-1 decompositions of symmetric positive semidefinite matrices, explicit descriptions and polynomial-time algorithms for ℓ2-optimal dictionaries are provided. result Explicit descriptions and polynomial-time algorithms for ℓ2-optimal dictionaries are provided.