Add to ORKGMany real-world problems can be formulated as the alignment between two geometric patterns. Previously, a great amount of research focus on the alignment of 2D or 3D patterns in the field of computer vision. Recently, the alignment problem in high dimensions finds several novel applications in practice. However, the research is still rather limited in the algorithmic aspect. To the best of our knowledge, most existing approaches are just simple extensions of their counterparts for 2D and 3D cases, and often suffer from the issues such as high computational complexities. In this paper, we propose an effective framework to compress the high dimensional geometric patterns. Any existing alignment method can be applied to the compressed geometri...
Many machine learning problems involve dealing with a large amount of high-dimensional data across d...
High dimensional data is usually produced by the source that only enjoys a limited number of degrees...
Joint data alignment is often regarded as a data simplification process. This idea is powerful and g...
In real-world, many problems can be formulated as the alignment between two geometric patterns. Prev...
Local alignment-free sequence comparison arises in the context of identifying similar seg-ments of s...
We introduce Joint Multidimensional Scaling, a novel approach for unsupervised manifold alignment, w...
We introduce Joint Multidimensional Scaling, a novel approach for unsupervised manifold alignment, w...
Having found the maximal matches between x and y, we can put some of them together to form a chain. ...
Abstract—Over the past few decades, dimensionality reduction has been widely exploited in computer v...
This paper presents a novel unsupervised domain adaptation method for cross-domain visual recognitio...
The goal of dimensionality reduction or manifold learning for a given set of high-dimensional data p...
Over the past few decades, dimensionality reduction has been widely exploited in computer vision and...
We present a new manifold learning algorithm called Local Orthogonality Preserving Alignment (LOPA)....
Many machine learning problems involve dealing with a large amount of high-dimensional data across d...
International audienceThe Wasserstein distance received a lot of attention recently in the community...
Many machine learning problems involve dealing with a large amount of high-dimensional data across d...
High dimensional data is usually produced by the source that only enjoys a limited number of degrees...
Joint data alignment is often regarded as a data simplification process. This idea is powerful and g...
In real-world, many problems can be formulated as the alignment between two geometric patterns. Prev...
Local alignment-free sequence comparison arises in the context of identifying similar seg-ments of s...
We introduce Joint Multidimensional Scaling, a novel approach for unsupervised manifold alignment, w...
We introduce Joint Multidimensional Scaling, a novel approach for unsupervised manifold alignment, w...
Having found the maximal matches between x and y, we can put some of them together to form a chain. ...
Abstract—Over the past few decades, dimensionality reduction has been widely exploited in computer v...
This paper presents a novel unsupervised domain adaptation method for cross-domain visual recognitio...
The goal of dimensionality reduction or manifold learning for a given set of high-dimensional data p...
Over the past few decades, dimensionality reduction has been widely exploited in computer vision and...
We present a new manifold learning algorithm called Local Orthogonality Preserving Alignment (LOPA)....
Many machine learning problems involve dealing with a large amount of high-dimensional data across d...
International audienceThe Wasserstein distance received a lot of attention recently in the community...
Many machine learning problems involve dealing with a large amount of high-dimensional data across d...
High dimensional data is usually produced by the source that only enjoys a limited number of degrees...
Joint data alignment is often regarded as a data simplification process. This idea is powerful and g...