Manifold clustering, which regards clusters as groups of points around compact manifolds, has been realized as a promising generalization of traditional clustering. A number of linear or nonlinear manifold clustering approaches have been developed recently. Although they have attained better performances than traditional clustering methods in many scenarios, most of these approaches suffer from...

This paper advocates a novel framework for segmenting a dataset in a Riemannian manifold M into clusters lying around low-dimensional submanifolds of M . Important examples of M , for which the proposed clustering algorithm is computationally efficient, are the sphere, the set of positive definite matrices, and the Grassmannian. The clustering problem with these examples of M is already useful ...

This paper gives an attempt to explore the manifold in the label space for multi-label learning. Traditional label space is logical, where no manifold exists. In order to study the label manifold, the label space should be extended to a Euclidean space. However, the label manifold is not explicitly available from the training examples. Fortunately, according to the smoothness assumption that th...

In this paper, we proposed a new semi-supervised multi-manifold learning method, called semisupervised sparse multi-manifold embedding (S3MME), for dimensionality reduction of hyperspectral image data. S3MME exploits both the labeled and unlabeled data to adaptively find neighbors of each sample from the same manifold by using an optimization program based on sparse representation, and naturall...

human action recognition is an important problem in computer vision. one of the methods that are recently used is sparse coding. conventional sparse coding algorithms learn dictionaries and codes in an unsupervised manner and neglect class information that is available in the training set. but in this paper for solving this problem, we use a discriminative sparse code based on multi-manifolds. ...

Multi-view clustering has received a lot of attentions in data mining recently. Though plenty works have been investigated on this topic, it is still severe challenge due to the complex nature multiple heterogeneous features. Particularly, existing multi-view algorithms fail consider topological structure data, which essential for manifold. In paper, we propose exploit implied manifold by learn...

This paper presents Manifold benchmarking suite, which is useful to measure and compare the performance of state-of-the-art multi core processors based systems. Manifold targets traditional single core as well as more recent multi core processors. In addition, it is equally useful for uniprocessor as well as symmetric multiprocessor (SMP) systems. Manifold measures hardware, operating system, a...

Multi-view representation learning attempts to learn a from multiple views and most existing methods are unsupervised. However, learned only unlabeled data may not be discriminative enough for further applications (e.g., clustering classification). For this reason, semi-supervised which could use along with the labeled multi-view need developed. Manifold information plays an important role in l...