[PDF] Top 20 Sparse and Low-Rank Modeling on High Dimensional Data: A Geometric Perspective.
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Sparse and Low-Rank Modeling on High Dimensional Data: A Geometric Perspective.
... large high dimensional dataset therefore always in- volves addressing the ...the data mani- fold from the given samples, and therefore have a nonlinear dimension reduction on the dataset [18] [1] ... See full document
136
Scalable Interpretable Multi-Response Regression via SEED
... big data applications such as genome-wide association studies and social media ...of sparse reduced-rank regression remains largely ...top-r sparse singu- lar value decomposition from a ... See full document
34
Large covariance estimation by thresholding principal orthogonal complements
... of high-dimensional covariance with a conditional sparsity structure, which is the composition of a low-rank matrix plus a sparse ...assuming sparse error covariance matrix in a ... See full document
48
High Resolution Satellite Image Classification Using Multi-Task Joint Sparse and Low-Rank Representation
... of High- Resolution Satellite (HRS) remotely sensed ...joint sparse and low -rank representation model to combine the strength of multiple features for HRS image ...consider sparse and ... See full document
20
Singular Value Decomposition for High Dimensional Data
... the high dimensional setting, statistical estimation is not possible without the assumption of strong structure in the ...vector data un- der Gaussian sequence models (Johnstone, 2011), but even more ... See full document
106
Joint analysis of multiple high-dimensional data types using sparse matrix approximations of rank-1 with applications to ovarian and liver cancer
... given data type obtained from a collection of n > 1 samples can be organized into a p × n data matrix D with rows representing vari- ables and columns representing measurements of the p variables in each ... See full document
28
Identification of influential observations in high-dimensional cancer survival data through the rank product test
... the rank product was ...the geometric mean of upper and lower bounds, defined recursively [16], since the algorithm provides accurate approximate p-values for the rank product when compared to the ... See full document
14
Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data
... These sparse representations have been used in a variety of statistical signal processing tasks, compressed sensing, machine learning (see ...learn data-driven dictionaries (see Gribonval et ...on ... See full document
63
Rank Determination for Low-Rank Data Completion
... large high-dimensional data is becoming more indis- pensable as hyperspectral images and videos, product ranking datasets and other applications of big datasets are attracting more attention ...the ... See full document
29
A Novel Singing Voice Separation Method Based on Sparse Non-Negative Matrix Factorization and Low-Rank Modeling
... music data using SNMF in combination with low-rank modeling is used in our separation ...imposing sparse constraint to the learning process and using domain adaptation technique to ... See full document
11
Recovering PCA and Sparse PCA via Hybrid-(l1,l2) Sparse Sampling of Data Elements
... a low dimensional subspace of the data such that projection of the data onto this subspace preserves as much information as ...are sparse principal components (SPCA). To derive ... See full document
34
Language Modeling with Power Low Rank Ensembles
... power low rank en- sembles (PLRE), in which low rank tensors are used to produce smoothed estimates for n-gram ...the low rank structures to discover semantic and syntactic ... See full document
12
Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python
... For sparse logistic regression using nonconvex regularizers, picasso achieves comparable objective value with ncvreg, and significantly outperforms ncvreg in timing ...for sparse logistic regression using ... See full document
5
A Signal Recovery Approach with Block Compressive Sensing Based on Image Steganalysis
... vertical low-pass subband (L) and a vertical high-pass subband ...the low-pass subband L and 1-D DLWT on each row of the high-pass subband ...one low-pass subband (LL) and three ... See full document
9
Angle and Time of Arrival Characteristics of 3D Air-to-Ground Radio Propagation Environments
... for high speed, reliable, low cost and all-time network ...of high speed internet in aircrafts has also increased ...range, high speed, and reliable link is not only needed for provision of ... See full document
32
Automated Data Imputation: Extending Low Rank Matrix Imputation Techniques For Statistical Prediction Modeling.
... of data sets. For that purpose, we chose to use a low-rank matrix approximation, also referred to as matrix ...Automated Data Imputation (ADI) and implement it in JMP Pro ...their data ... See full document
100
Yang, Yinchong (2018): Enhancing representation learning with tensor decompositions for knowledge graphs and high dimensional sequence modeling. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik
... second data situation that requires efficient representation learning: high dimensional sequential ...longitudinal– data are the outcomes of measuring the same features repeatedly at ... See full document
133
Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression
... learning high-dimensional tensor regression prob- lems with low-rank ...learning high- dimensional models is computation since the underlying optimization problems are often ... See full document
37
Design and Implementation of Sensitive Information Security Model based on Term Clustering
... If such low dimensional data is provided very high security, no doubt with the increased complexity of the system, the system is able to send the high dimensional and delicate informatio[r] ... See full document
6
A new approach for data visualization problem
... multidimensional data making use of humans’ natural visual ...specifically, data visualization reveals relationships in data sets that are not evident from the raw data, by using mathematical ... See full document
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