Add to ORKGCurrent manifold alignment methods can effectively align data sets that are drawn from a non-intersecting set of manifolds. However, as data sets become increasingly high-dimensional and complex, this assumption may not hold. This paper proposes a novel manifold alignment algorithm, low rank alignment (LRA), that uses a low rank representation (instead of a nearest neighbor graph construction) to embed and align data sets drawn from mixtures of manifolds. LRA does not require the tuning of a sensitive nearest neighbor hyperparameter or prior knowledge of the number of manifolds, both of which are common drawbacks with existing techniques. We demonstrate the effectiveness of our algorithm in two real-world applications: a transfer learning t...
© 2016, Springer-Verlag Berlin Heidelberg. In this paper, we propose a robust unsupervised algorithm...
Locally Linear Embedding (LLE) is an effective method for both single manifold embedding and multipl...
We introduce Joint Multidimensional Scaling, a novel approach for unsupervised manifold alignment, w...
High dimensional data is usually produced by the source that only enjoys a limited number of degrees...
Many machine learning problems involve dealing with a large amount of high-dimensional data across d...
Manifold alignment has been found to be useful in many elds of machine learning and data mining. In ...
In this paper, we propose a novel manifold alignment method by learning the underlying common manifo...
In this paper, we study a family of semisupervised learning algorithms for "aligning" di...
Abstract. In this paper, we study the problem of manifold alignment, which aims at “aligning ” diffe...
Manifold learning has been demonstrated as an effective way to represent intrinsic geometrical struc...
Manifold learning has been demonstrated as an effective way to represent intrinsic geometrical struc...
The goal of dimensionality reduction or manifold learning for a given set of high-dimensional data p...
AbstractManifold alignment is useful to extract the shared latent structure among multiple data sets...
Image clustering methods are efficient tools for applications such as content-based image retrieval ...
We introduce Joint Multidimensional Scaling, a novel approach for unsupervised manifold alignment, w...
© 2016, Springer-Verlag Berlin Heidelberg. In this paper, we propose a robust unsupervised algorithm...
Locally Linear Embedding (LLE) is an effective method for both single manifold embedding and multipl...
We introduce Joint Multidimensional Scaling, a novel approach for unsupervised manifold alignment, w...
High dimensional data is usually produced by the source that only enjoys a limited number of degrees...
Many machine learning problems involve dealing with a large amount of high-dimensional data across d...
Manifold alignment has been found to be useful in many elds of machine learning and data mining. In ...
In this paper, we propose a novel manifold alignment method by learning the underlying common manifo...
In this paper, we study a family of semisupervised learning algorithms for "aligning" di...
Abstract. In this paper, we study the problem of manifold alignment, which aims at “aligning ” diffe...
Manifold learning has been demonstrated as an effective way to represent intrinsic geometrical struc...
Manifold learning has been demonstrated as an effective way to represent intrinsic geometrical struc...
The goal of dimensionality reduction or manifold learning for a given set of high-dimensional data p...
AbstractManifold alignment is useful to extract the shared latent structure among multiple data sets...
Image clustering methods are efficient tools for applications such as content-based image retrieval ...
We introduce Joint Multidimensional Scaling, a novel approach for unsupervised manifold alignment, w...
© 2016, Springer-Verlag Berlin Heidelberg. In this paper, we propose a robust unsupervised algorithm...
Locally Linear Embedding (LLE) is an effective method for both single manifold embedding and multipl...
We introduce Joint Multidimensional Scaling, a novel approach for unsupervised manifold alignment, w...