sparse coding paradigmsvisual processing mapNon-negative matrix factorization (NMF) is a very efficient parameter-free method for decomposing multivariate data into strictly positive activations and basis vector
Köner, Sparse coding and nmf, in: IEEE International Conference on Neural Networks - Conference Proceedings, vol. 4, 2004, pp. 2529–2533. Google Scholar [3] I. Ohiorhenuan, F. Mechler, K. Purpura, A. Schmid, Q. Hu, J. Victor Sparse coding and high-order correlations in fine-...
We propose an efficient algorithm for the generalized sparse coding (SC) inference problem. The proposed framework applies to both the single dictionary setting, where each data point is represented as a sparse combination of the columns of one dictionary matrix, as well as the multiple dictionary...
sparsecoding,aridnon-negativeniat,rixfactorization(NMF).Someoft,hese nietbodshavetheirrootsinneuralcomputation,buthavesincebeenshown tobewidelyapplicableforsignalanalysis. Inthispaperweproposetocombinesparsecodingandnon-negativema- trixfactorizationintonownegativesparseding(NNSC). ...
“Efficient sparse coding algorithms,” presented at the Advances in Neural Information Processing Systems (2006) B. Efron et al. Least angle regression Ann. Statist. (2004) Z. Jiang et al. Learning a discriminative dictionary for sparse coding via label consistent K-SVDView more references Cited...
Multicollinearity refers to the presence of collinearity between multiple variables and renders the results of statistical inference erroneous (Type II error). This is particularly important in environmental health research where multicollinearity can hinder inference. To address this, correlated variables are...
sparse coding摘要 Hyperspectral image (HSI) unmixing has attracted increasing research interests in recent decades. The major difficulty of it lies in that the endmembers and the associated abundances need to be separated from highly mixed observation data with few a priori information. Recently, spar...
we propose a novel sparse coding based method to investigate brain dynamics of freely-behaving mice from the perspective of functional connectivity, using super-long local field potential (LFP) recordings from 13 distinct regions of the mouse brain. Compared with surrogate datasets, six and four rep...
In what follows, nonnegative matrix factorization (NMF) and its extensions to different regularization functions are introduced. Several approaches to group basis representation are addressed. Group sparse coding is surveyed. Then Bayesian learning methods for matrix factorization and other related tasks ar...
In reference [40], sparse coding was formulated, was used to select as few basic images as possible from the codebook to linearly reconstruct the new input image and minimize the reconstruction error and solved the click prediction problem. However, researchers have found that the performance of ...