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Found 13 papers, 8 papers with code
Match-And-Deform: Time Series Domain Adaptation through Optimal Transport and Temporal Alignment
While large volumes of unlabeled data are usually available, associated labels are often scarce.
Fast Optimal Transport through Sliced Wasserstein Generalized Geodesics
Wasserstein distance (WD) and the associated optimal transport plan have been proven useful in many applications where probability measures are at stake.
Hyperbolic Sliced-Wasserstein via Geodesic and Horospherical Projections
It has been shown beneficial for many types of data which present an underlying hierarchical structure to be embedded in hyperbolic spaces.
Unbalanced Optimal Transport through Non-negative Penalized Linear Regression
In particular, we consider majorization-minimization which leads in our setting to efficient multiplicative updates for a variety of penalties.
Partial Optimal Tranport with applications on Positive-Unlabeled Learning
Classical optimal transport problem seeks a transportation map that preserves the total mass between two probability distributions, requiring their masses to be equal.
Partial Optimal Transport with Applications on Positive-Unlabeled Learning
In this paper, we address the partial Wasserstein and Gromov-Wasserstein problems and propose exact algorithms to solve them.
Time Series Alignment with Global Invariances
Multivariate time series are ubiquitous objects in signal processing.
Sliced Gromov-Wasserstein
Recently used in various machine learning contexts, the Gromov-Wasserstein distance (GW) allows for comparing distributions whose supports do not necessarily lie in the same metric space.
Optimal Transport for structured data with application on graphs
This work considers the problem of computing distances between structured objects such as undirected graphs, seen as probability distributions in a specific metric space.
Ranked #3 on
Graph Classification
on NCI1
Combining multiple resolutions into hierarchical representations for kernel-based image classification
Such a representation allows one to benefit from the context information thanks to the coarser levels, and subregions spatial arrangement information thanks to the finer levels.
Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach
Nowadays, hyperspectral image classification widely copes with spatial information to improve accuracy.
Classification Of Hyperspectral Images
General Classification
+1
A Subpath Kernel for Learning Hierarchical Image Representations
This is particularly true in remote sensing, where such representations allow for revealing different objects of interest at various scales through a tree structure.
Dense Bag-of-Temporal-SIFT-Words for Time Series Classification
In this paper, we design a time series classification scheme that builds on the SIFT framework adapted to time series to feed a Bag-of-Words.
