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Found 14 papers, 2 papers with code
Analysis of Descent-Based Image Registration
We show that the area of this neighborhood increases at least quadratically with the smoothing filter size, which justifies the use of a smoothing step in image registration with local optimizers such as gradient descent.
A Study of Image Analysis with Tangent Distance
As theoretical studies about the tangent distance algorithm have been largely overlooked, we present in this work a detailed performance analysis of this useful algorithm, which can eventually help its implementation.
Out-of-sample generalizations for supervised manifold learning for classification
Supervised manifold learning methods for data classification map data samples residing in a high-dimensional ambient space to a lower-dimensional domain in a structure-preserving way, while enhancing the separation between different classes in the learned embedding.
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction
Local learning of sparse image models has proven to be very effective to solve inverse problems in many computer vision applications.
A study of the classification of low-dimensional data with supervised manifold learning
Supervised manifold learning methods learn data representations by preserving the geometric structure of data while enhancing the separation between data samples from different classes.
Nonlinear Supervised Dimensionality Reduction via Smooth Regular Embeddings
In this work, we build on recent theoretical results on the generalization performance of supervised manifold learning algorithms.
Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification
A multilevel tree-structured discriminative dictionary is learnt for each class, with a learning objective concerning the reconstruction errors of the image patches around the pixels over each class-representative dictionary.
Domain Adaptation on Graphs by Learning Aligned Graph Bases
Although the semi-supervised estimation of class labels is an ill-posed problem in general, in several applications it is possible to find a source graph on which the label function has similar frequency content to that on the target graph where the actual classification problem is defined.
Domain Adaptation on Graphs by Learning Graph Topologies: Theoretical Analysis and an Algorithm
We first propose a theoretical analysis of domain adaptation on graphs and present performance bounds that characterize the target classification error in terms of the properties of the graphs and the data manifolds.
Mask Combination of Multi-layer Graphs for Global Structure Inference
Structure inference is an important task for network data processing and analysis in data science.
Graph Domain Adaptation with Localized Graph Signal Representations
We estimate the unknown target labels by solving an optimization problem where the label information is transferred from the source graph to the target graph based on the prior that the projections of the label functions onto localized graph bases be similar between the source and the target graphs.
Learning Multi-Modal Nonlinear Embeddings: Performance Bounds and an Algorithm
While many approaches exist in the literature to learn low-dimensional representations for data collections in multiple modalities, the generalizability of multi-modal nonlinear embeddings to previously unseen data is a rather overlooked subject.
Learning Graph ARMA Processes from Time-Vertex Spectra
The modeling of time-varying graph signals as stationary time-vertex stochastic processes permits the inference of missing signal values by efficiently employing the correlation patterns of the process across different graph nodes and time instants.
Locally Stationary Graph Processes
In this work, we propose a locally stationary graph process (LSGP) model that aims to extend the classical concept of local stationarity to irregular graph domains.
