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Found 10 papers, 0 papers with code
Semi-supervised segmentation of land cover images using nonlinear canonical correlation analysis with multiple features and t-SNE
This suggests that, in order to account for spatial correlation between pixels, a feature vector associated with each pixel may be a vectorized tensor representing the multiple bands and a local patch as appropriate.
Structured Radial Basis Function Network: Modelling Diversity for Multiple Hypotheses Prediction
A closed-form solution with least-squares is presented, which to the authors knowledge, is the fastest solution in the literature for multiple hypotheses and structured predictions.
Continual learning-based probabilistic slow feature analysis for multimode dynamic process monitoring
In this paper, a novel multimode dynamic process monitoring approach is proposed by extending elastic weight consolidation (EWC) to probabilistic slow feature analysis (PSFA) in order to extract multimode slow features for online monitoring.
Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation
By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP-MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process monitoring.
An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring
To realize this purpose, the technique of a mixture of probabilistic principal component analysers is utilized to establish the model of the underlying nonlinear process with local PPCA models, where a novel composite monitoring statistic is proposed based on the integration of two monitoring statistics in modified PPCA-based fault detection approach.
Coupling Matrix Manifolds and Their Applications in Optimal Transport
These geometrical features of CMM have paved the way for developing numerical Riemannian optimization algorithms such as Riemannian gradient descent and Riemannian trust-region algorithms, forming a uniform optimization method for all types of OT problems.
Sparse Least Squares Low Rank Kernel Machines
A general framework of least squares support vector machine with low rank kernels, referred to as LR-LSSVM, is introduced in this paper.
l1-norm Penalized Orthogonal Forward Regression
A l1-norm penalized orthogonal forward regression (l1-POFR) algorithm is proposed based on the concept of leaveone- out mean square error (LOOMSE).
Low Rank Representation on Riemannian Manifold of Square Root Densities
In this paper, we present a novel low rank representation (LRR) algorithm for data lying on the manifold of square root densities.
Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization
In contrast to existing techniques, we propose a new clustering algorithm that alternates between different modes of the proposed heterogeneous tensor model.
