sparse modeling
Sparse Modeling of Neural Network Posterior Probabilities for Exemplar-based Speech Recognition
... posterior-based sparse modeling framework with an equivalent DTW tem- plate matching system that uses Euclidean distance ...based sparse modeling framework outperforms the simi- lar DTW ...
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See All by Looking at A Few: Sparse Modeling for Finding Representative Objects
... as Sparse Modeling Representative Selection (SMRS), with several standard methods for finding representatives of datasets: Kmedoids, Rank Revealing QR (RRQR) and sim- ple random selection of training data ...
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From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
... optimally sparse solutions can be found by con- crete, effective computational ...have sparse solutions. Hence, ad- vances in finding sparse solutions to underdetermined systems have energized research ...
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Sparse Modeling of Posterior Exemplars for Keyword Detection
... The sparse recovery process implicitly leads to a compe- tition between the two subspaces of keyword and background ...recovered sparse representation leads to better discrimination and enables detection of ...
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Sparse modeling: some unifying theory and word-imaging
... • Chosen predictor words form a set known as the Word Image for q. • Word image must be evaluated two ways:[r] ...
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Sparse Subspace Modeling for Query by Example Spoken Term Detection
... for sparse representation provides an unsupervised, yet effective, way of extracting an over-complete basis set to model the underlying ...of sparse modeling ...
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Sparse and Low-Rank Modeling on High Dimensional Data: A Geometric Perspective.
... criminant spatial features among frames [59] [78] [61] [99]. Each video sequence is hence considered as a set of feature vectors, and the distance between two videos is determined by the two sets. Coupled with ...
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Sparse coding
... with sparse codes, we could pick a random codeword from a set of low activity ratio ...code". Sparse coding is therefore closely related to Independent Component Analysis (Bell and Sejnowski 1997, ...
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A Novel Singing Voice Separation Method Based on Sparse Non-Negative Matrix Factorization and Low-Rank Modeling
... low-rank modeling is used in our separation ...imposing sparse constraint to the learning process and using domain adaptation technique to reduce the mismatch effect between train and test circumstances ...
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Modeling association between multivariate correlated outcomes and high-dimensional sparse covariates: the adaptive SVS method
... In this paper we focus on the problem of modeling and discovering association between genotypes, or in principal any covariates of interest, and multivariate correlated pheno- types. We focus on exploratory ...
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Sparse Boosting
... For high-dimensional linear regression (or classification) problems with many ineffective pre- dictor variables, the Lasso estimate can be very poor in terms of prediction accuracy and as a variable selection method, see ...
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Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory
... sian errors are assumed both in the process and measurement models for such problems. We show that the extended framework allows arbitrary PLQ densities to be used, and that the proposed IP ap- proach solves the ...
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Sparse Activity and Sparse Connectivity in Supervised Learning
... where sparse activity and sparse connectivity are used to enhance classification ...pre-defined sparse- ness for any given ...by sparse activity or sparse ...
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Stabilized Sparse Online Learning for Sparse Data
... We evaluate the performance of our algorithm on several real-world classification data sets with up to 100, 000 features. These data sets have different levels of sparsity with various sample sizes. The information of ...
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Recovering sparse graphs
... The work presented in this paper is motivated by the line of research on algorithmic metatheorems, general algorithmic results that guarantee the existence of efficient algorithms for wide classes of problems. The most ...
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Noisy Sparse Subspace Clustering
... While our analysis is based upon the geometric analysis of Soltanolkotabi et al. (2012), the analysis is more involved: In SSC, sample points are used as the dictionary for sparse recovery, and therefore noisy SSC ...
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A Class of Parallel Multilevel Sparse Approximate Inverse Preconditioners for Sparse Linear Systems
... The incomplete LU (ILU) factorizations have been used as general purpose preconditioners for solving general sparse matrices [28]. Since the ILU preconditioners are based on various Gauss elimination procedures, ...
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Sparse Adversarial Perturbations for Videos
... Technically, the main difference between videos and im- ages lies in the temporal structure contained in videos. Therefore, a properly designed attacking method should ex- plore the temporal information to achieve ...
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Communication-efficient Sparse Regression
... We devise a communication-efficient approach to distributed sparse regression in the high- dimensional setting. The key idea is to average “debiased” or “desparsified” lasso esti- mators. We show the approach ...
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Sparse Single-Index Model
... The use of MCMC methods has become a popular way to compute Bayesian estimates. For an introduction to the domain, one should refer to the comprehensive monograph of Marin and Robert (2007) and the references therein. ...
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