Manifold alignment is useful to extract the shared latent structure among multiple data sets and the similarity among different datasets. As many kinds of real world data can be analyzed using low dimensional represen...Manifold alignment is useful to extract the shared latent structure among multiple data sets and the similarity among different datasets. As many kinds of real world data can be analyzed using low dimensional representations, manifold alignment algorithms can be used in a wide range of applications, such as data mining. In this paper, we propose a three-stage approach to manifold alignment using discrete surface Ricci flow. Our approach transforms the original intrinsic manifolds to hyper spheres using confonnal mapping in the first stage, and then zooms these hyper spheres into the same scale and aligns them in the following stages. We describe in details about our algorithm, its theoretical principles, our experimental results, and the comparison to previous alignment methods. To prove the effectiveness of our algorithm, three kinds of experiments are presented, including a toy dataset, one containing parallel corpus of parliament proceedings and another containing both images and texts. With these experiments, the latent utility in discovering the similarity among different kinds of data sets can be demonstrated, whether within the same kind of data or across different kinds of modals of data.展开更多
Unsupervised image translation(UIT)studies the mapping between two image domains.Since such mappings are under-constrained,existing research has pursued various desirable properties such as distributional matching or ...Unsupervised image translation(UIT)studies the mapping between two image domains.Since such mappings are under-constrained,existing research has pursued various desirable properties such as distributional matching or two-way consistency.In this paper,we re-examine UIT from a new perspective:distributional semantics consistency,based on the observation that data variations contain semantics,e.g.,shoes varying in colors.Further,the semantics can be multi-dimensional,e.g.,shoes also varying in style,functionality,etc.Given two image domains,matching these semantic dimensions during UIT will produce mappings with explicable correspondences,which has not been investigated previously.We propose distributional semantics mapping(DSM),the first UIT method which explicitly matches semantics between two domains.We show that distributional semantics has been rarely considered within and beyond UIT,even though it is a common problem in deep learning.We evaluate DSM on several benchmark datasets,demonstrating its general ability to capture distributional semantics.Extensive comparisons show that DSM not only produces explicable mappings,but also improves image quality in general.展开更多
文摘Manifold alignment is useful to extract the shared latent structure among multiple data sets and the similarity among different datasets. As many kinds of real world data can be analyzed using low dimensional representations, manifold alignment algorithms can be used in a wide range of applications, such as data mining. In this paper, we propose a three-stage approach to manifold alignment using discrete surface Ricci flow. Our approach transforms the original intrinsic manifolds to hyper spheres using confonnal mapping in the first stage, and then zooms these hyper spheres into the same scale and aligns them in the following stages. We describe in details about our algorithm, its theoretical principles, our experimental results, and the comparison to previous alignment methods. To prove the effectiveness of our algorithm, three kinds of experiments are presented, including a toy dataset, one containing parallel corpus of parliament proceedings and another containing both images and texts. With these experiments, the latent utility in discovering the similarity among different kinds of data sets can be demonstrated, whether within the same kind of data or across different kinds of modals of data.
基金supported by National Natural Science Foundation of China(Grant No.61772462)the 100 Talents Program of Zhejiang University。
文摘Unsupervised image translation(UIT)studies the mapping between two image domains.Since such mappings are under-constrained,existing research has pursued various desirable properties such as distributional matching or two-way consistency.In this paper,we re-examine UIT from a new perspective:distributional semantics consistency,based on the observation that data variations contain semantics,e.g.,shoes varying in colors.Further,the semantics can be multi-dimensional,e.g.,shoes also varying in style,functionality,etc.Given two image domains,matching these semantic dimensions during UIT will produce mappings with explicable correspondences,which has not been investigated previously.We propose distributional semantics mapping(DSM),the first UIT method which explicitly matches semantics between two domains.We show that distributional semantics has been rarely considered within and beyond UIT,even though it is a common problem in deep learning.We evaluate DSM on several benchmark datasets,demonstrating its general ability to capture distributional semantics.Extensive comparisons show that DSM not only produces explicable mappings,but also improves image quality in general.
