Add to ORKGIt is a challenging problem to cluster multi- and high-dimensional data with complex intrinsic properties and non-linear manifold structure. The recently proposed subspace clustering method, Low Rank Representation (LRR), shows attractive performance on data clustering, but it generally does with data in Euclidean spaces. In this paper, we intend to cluster complex high dimensional data with multiple varying factors. We propose a novel representation, namely Product Grassmann Manifold (PGM), to represent these data. Additionally, we discuss the geometry metric of the manifold and expand the conventional LRR model in Euclidean space onto PGM and thus construct a new LRR model. Several clustering experimental results show that the proposed m...
Abstract. Manifold clustering, which regards clusters as groups of points around compact manifolds, ...
Relationships between entities in datasets are often of multiple nature, like geographical distance,...
Relationships between entities in datasets are often of multiple nature, like geographical distance,...
The higher-order clustering problem arises when data is drawn from multiple subspaces or when observ...
Image sets and videos can be modeled as subspaces, which are actually points on Grassmann manifolds....
An important research topic of the recent years has been to understand and analyze data collections ...
Image sets and videos can be modeled as subspaces which are actually points on Grassmann manifolds. ...
An important research topic of the recent years has been to understand and analyze manifold-modeled ...
An important tool in high-dimensional, explorative data mining is given by clustering methods. They ...
An important tool in high-dimensional, explorative data mining is given by clustering methods. They ...
Nowadays we are in the big data era,where the data is usually high dimensional.How to process high d...
Data clustering is an important research topic in data mining and signal processing communications. ...
Because of variable dependence, high dimensional data typically have much lower intrinsic dimensiona...
Abstract—Relationships between entities in datasets are often of multiple nature, like geographical ...
With the aim of improving the clustering of data (such as image sequences) lying on Grassmann manifo...
Abstract. Manifold clustering, which regards clusters as groups of points around compact manifolds, ...
Relationships between entities in datasets are often of multiple nature, like geographical distance,...
Relationships between entities in datasets are often of multiple nature, like geographical distance,...
The higher-order clustering problem arises when data is drawn from multiple subspaces or when observ...
Image sets and videos can be modeled as subspaces, which are actually points on Grassmann manifolds....
An important research topic of the recent years has been to understand and analyze data collections ...
Image sets and videos can be modeled as subspaces which are actually points on Grassmann manifolds. ...
An important research topic of the recent years has been to understand and analyze manifold-modeled ...
An important tool in high-dimensional, explorative data mining is given by clustering methods. They ...
An important tool in high-dimensional, explorative data mining is given by clustering methods. They ...
Nowadays we are in the big data era,where the data is usually high dimensional.How to process high d...
Data clustering is an important research topic in data mining and signal processing communications. ...
Because of variable dependence, high dimensional data typically have much lower intrinsic dimensiona...
Abstract—Relationships between entities in datasets are often of multiple nature, like geographical ...
With the aim of improving the clustering of data (such as image sequences) lying on Grassmann manifo...
Abstract. Manifold clustering, which regards clusters as groups of points around compact manifolds, ...
Relationships between entities in datasets are often of multiple nature, like geographical distance,...
Relationships between entities in datasets are often of multiple nature, like geographical distance,...