Papers Sparse Learning
“Sparse Learning” 태그가 달린 논문 209편 · 필터 해제
Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning
Implicit discourse relation recognition aims to understand and annotate the latent relations between two discourse arguments, such as temporal, comparison, etc. Most previous methods encode two discourse arguments separa…
RelationSparse LearningText SummarizationSparse Range-constrained Learning and Its Application for Medical Image Grading
Sparse learning has been shown to be effective in solving many real-world problems. Finding sparse representations is a fundamentally important topic in many fields of science including signal processing, computer vision…
Sparse LearningSafe Active Feature Selection for Sparse Learning
We present safe active incremental feature selection~(SAIF) to scale up the computation of LASSO solutions. SAIF does not require a solution from a heavier penalty parameter as in sequential screening or updating the ful…
feature selectionSparse LearningSafe Element Screening for Submodular Function Minimization
Submodular functions are discrete analogs of convex functions, which have applications in various fields, including machine learning and computer vision. However, in large-scale applications, solving Submodular Function …
Combinatorial OptimizationSparse LearningStructure-preserving Guided Retinal Image Filtering and Its Application for Optic Disc Analysis
Retinal fundus photographs have been used in the diagnosis of many ocular diseases such as glaucoma, pathological myopia, age-related macular degeneration and diabetic retinopathy. With the development of computer scienc…
Optic Cup SegmentationSparse LearningLearning on Hypergraphs with Sparsity
Hypergraph is a general way of representing high-order relations on a set of objects. It is a generalization of graph, in which only pairwise relations can be represented. It finds applications in various domains where r…
Sparse LearningLocally Convex Sparse Learning over Networks
We consider a distributed learning setup where a sparse signal is estimated over a network. Our main interest is to save communication resource for information exchange over the network and reduce processing time. Each n…
Sparse LearningProximal SCOPE for Distributed Sparse Learning: Better Data Partition Implies Faster Convergence Rate
Distributed sparse learning with a cluster of multiple machines has attracted much attention in machine learning, especially for large-scale applications with high-dimensional data. One popular way to implement sparse le…
Sparse LearningFast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms
The $L_0$-regularized least squares problem (a.k.a. best subsets) is central to sparse statistical learning and has attracted significant attention across the wider statistics, machine learning, and optimization communit…
Combinatorial OptimizationSparse LearningVariable SelectionA graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data
Gene expression data represents a unique challenge in predictive model building, because of the small number of samples $(n)$ compared to the huge amount of features $(p)$. This "$n<<p$" property has hampered application…
Classificationfeature selectionGeneral ClassificationRobust classification+1Graph Autoencoder-Based Unsupervised Feature Selection with Broad and Local Data Structure Preservation
Feature selection is a dimensionality reduction technique that selects a subset of representative features from high dimensional data by eliminating irrelevant and redundant features. Recently, feature selection combined…
Dimensionality Reductionfeature selectionSparse LearningStrongly Hierarchical Factorization Machines and ANOVA Kernel Regression
High-order parametric models that include terms for feature interactions are applied to various data mining tasks, where ground truth depends on interactions of features. However, with sparse data, the high- dimensional …
parameter estimationregressionSparse LearningA New Data-Driven Sparse-Learning Approach to Study Chemical Reaction Networks
Chemical kinetic mechanisms can be represented by sets of elementary reactions that are easily translated into mathematical terms using physicochemical relationships. The schematic representation of reactions captures th…
Sparse LearningSuper-sparse Learning in Similarity Spaces
In several applications, input samples are more naturally represented in terms of similarities between each other, rather than in terms of feature vectors. In these settings, machine-learning algorithms can become very c…
General ClassificationSparse LearningA Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks
In this paper, we propose an optimization-based sparse learning approach to identify the set of most influential reactions in a chemical reaction network. This reduced set of reactions is then employed to construct a red…
Sparse LearningSparse learning of stochastic dynamic equations
With the rapid increase of available data for complex systems, there is great interest in the extraction of physically relevant information from massive datasets. Recently, a framework called Sparse Identification of Non…
DenoisingSparse LearningParametric Simplex Method for Sparse Learning
High dimensional sparse learning has imposed a great computational challenge to large scale data analysis. In this paper, we investiage a broad class of sparse learning approaches formulated as linear programs parametriz…
Sparse LearningOn Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning
We propose a DC proximal Newton algorithm for solving nonconvex regularized sparse learning problems in high dimensions. Our proposed algorithm integrates the proximal newton algorithm with multi-stage convex relaxation …
Sparse LearningNonsparse learning with latent variables
As a popular tool for producing meaningful and interpretable models, large-scale sparse learning works efficiently when the underlying structures are indeed or close to sparse. However, naively applying the existing regu…
Model SelectionSparse LearningCommunication-efficient Algorithm for Distributed Sparse Learning via Two-way Truncation
We propose a communicationally and computationally efficient algorithm for high-dimensional distributed sparse learning. At each iteration, local machines compute the gradient on local data and the master machine solves …
Sparse LearningVocal Bursts Valence Prediction