[PDF] Top 20 LLORMA: Local Low-Rank Matrix Approximation
Has 10000 "LLORMA: Local Low-Rank Matrix Approximation" found on our website. Below are the top 20 most common "LLORMA: Local Low-Rank Matrix Approximation".
LLORMA: Local Low-Rank Matrix Approximation
... Other local learning paradigms were suggested in the context dimensionality such as local principal component analysis (Kambhatla and Leen, 1997) and local linear embedding (LLE) (Roweis and Saul, ... See full document
24
Low-Rank Doubly Stochastic Matrix Decomposition for Cluster Analysis
... We have compared the above DCD selection method with several other clustering eval- uation methods: Calinski-Harabasz (CH; Calinski and Harabasz, 1974), Davies-Bouldin (DB; Davies and Bouldin, 1979) and gap statistics ... See full document
25
Introducing a New Hybrid Adaptive Local Optimal Low Rank Approximation Method for Denoising Images
... Also, according to the results obtained in this section, the results of the proposed method is comparable and even superior to the state-of-the-art denoising methods in terms of PSNR, FSIM and edge preservation. This is ... See full document
14
Computing Approximation GCD of Several Polynomials by Structured Total Least Norm
... that approximation GCD of se- veral polynomials can be solved by a practical and re- liable way based on STLN method and transformed to the approximation of Sylvester structure ...structured matrix ... See full document
8
Clustering Over Multiple Evolving Data Streams of the Traffic Cyber-Physical Systems
... by low rank approximation Matrix Factorization (IC-MF), which can monitor the distribution of clusters over multiple sensor data streams based on their ...the low-rank ... See full document
16
INFORMATION TECHNOLOGY GOVERNANCE USING COBIT 4 0 DOMAIN DELIVERY SUPPORT AND MONITORING EVALUATION
... the low-rank matrix approximation problem where the low-rank matrix is formed by the nonlocal similarity ...this low-rank optimization problem constrained by ... See full document
11
Parameter Tuning via Kernel Matrix Approximation for Support Vector Machine
... training set and validation set. For every kernel parameter , we use the training set and the corresponding labels to generate the kernel matrix Q . Second, we apply MoCIC to compute the low-rank ... See full document
8
Spatial Aggregation Facilitates Discovery of Spatial Topics
... through matrix factorization lenses we show that spatial aggregation allows low rank approximation of the original document-word matrix, in which spatially distinct topics are preserved ... See full document
11
Global denoising for 3D MRI
... of low-rank matrix completion, which is derived from compressed sensing theory [12], were pro- posed to accomplish the accurate restoration from the whole 3D dataset ...effectively. ... See full document
17
Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery
... the low-rank matrix recovery problem is based on a quadratic lift: replacing XX T in (2) by a convex term M 0, and augmenting the objective with a trace penalty λ ·tr(M) to induce a ... See full document
34
Sparse and smooth canonical correlation analysis through rank 1 matrix approximation
... In Figs. 6 and 7, the normalized mean square error is plotted versus the SNR for the proposed approaches and state of the art algorithm. It is clearly shown that our sparse CCA based on rank-1 matrix ... See full document
14
Completing Any Low-rank Matrix, Provably
... of approximation power, provided measurements from the sensing basis are sub- sampled according to their coherence with the sparsity ...of local coherence sampling seems to have originated in Rauhut and ... See full document
36
The Algebraic Combinatorial Approach for Low-Rank Matrix Completion
... common low-rank as- sumption - that is, under the model assumption (or approximation) that there is an under- lying complete matrix of some low rank r from which the partial ... See full document
46
On the equivalence between Total Least Squares and Maximum Likelihood PCA
... weighted low rank matrix approximation where the weight is derived from the distribution of the errors in the ...weighted low rank approximation (WLRA), the MLPCA, the ... See full document
14
Memory Efficient Kernel Approximation
... whether low-rank approximation is the most memory efficient way to approximate a kernel ...from low-rank to block- diagonal as the scaling parameter γ varies from 0 to ...best ... See full document
32
Lower bounds for the low rank matrix approximation
... Low-rank approximation of a linear operator is ubiquitous in applied mathematics, sci- entific computing, numerical analysis, and a number of other ...a low- rank matrix could ... See full document
14
Compressed Sensing, Sparse Approximation, and Low-Rank Matrix Estimation
... unknown matrix cannot be too large or too small (the ratio between the top and lowest value must be ...trimmed matrix, truncates it as to only keep the top r singular values (note that the value of r is ... See full document
171
Simultaneous Pursuit of Sparseness and Rank Structures for Matrix Decomposition
... A low rank property of a matrix describes global information across different tasks, whereas sparseness concerns local information of specific ...the local information characterizes ... See full document
29
A non-linear structure-preserving matrix method for the computation of the coefficients of an approximate greatest common divisor of two Bernstein polynomials
... structured low rank approximation of the Sylvester ma- trix of two power basis polynomials has been considered by several researchers ...this matrix, S(f, g), for the Bernstein polynomials ... See full document
38
Bootstrap-Based Regularization for Low-Rank Matrix Estimation
... As our simulation study makes clear, the proposed methods have very different strengths and weaknesses. Both methods that apply a hard thresholding rule to the singular values, namely TSVD-k and TSVD-τ , provide accurate ... See full document
29
Related subjects