Subspace clustering by block diagonal representation
This paper studies the subspace clustering problem. Given some data points approximately
drawn from a union of subspaces, the goal is to group these data points into their underlying …
drawn from a union of subspaces, the goal is to group these data points into their underlying …
Rank-constrained spectral clustering with flexible embedding
Spectral clustering (SC) has been proven to be effective in various applications. However,
the learning scheme of SC is suboptimal in that it learns the cluster indicator from a fixed …
the learning scheme of SC is suboptimal in that it learns the cluster indicator from a fixed …
Beyond linear subspace clustering: A comparative study of nonlinear manifold clustering algorithms
Subspace clustering is an important unsupervised clustering approach. It is based on the
assumption that the high-dimensional data points are approximately distributed around …
assumption that the high-dimensional data points are approximately distributed around …
Scalable sparse subspace clustering by orthogonal matching pursuit
Subspace clustering methods based on ell_1, l_2 or nuclear norm regularization have
become very popular due to their simplicity, theoretical guarantees and empirical success …
become very popular due to their simplicity, theoretical guarantees and empirical success …
A statistical perspective on algorithmic leveraging
One popular method for dealing with large-scale data sets is sampling. Using the empirical
statistical leverage scores as an importance sampling distribution, the method of algorithmic …
statistical leverage scores as an importance sampling distribution, the method of algorithmic …
Structured sparse subspace clustering: A joint affinity learning and subspace clustering framework
Subspace clustering refers to the problem of segmenting data drawn from a union of
subspaces. State-of-the-art approaches for solving this problem follow a two-stage …
subspaces. State-of-the-art approaches for solving this problem follow a two-stage …
Spectral–spatial sparse subspace clustering for hyperspectral remote sensing images
Clustering for hyperspectral images (HSIs) is a very challenging task due to its inherent
complexity. In this paper, we propose a novel spectral-spatial sparse subspace clustering S …
complexity. In this paper, we propose a novel spectral-spatial sparse subspace clustering S …
Oracle based active set algorithm for scalable elastic net subspace clustering
State-of-the-art subspace clustering methods are based on expressing each data point as a
linear combination of other data points while regularizing the matrix of coefficients with l_1 …
linear combination of other data points while regularizing the matrix of coefficients with l_1 …
Constructing the L2-graph for robust subspace learning and subspace clustering
Under the framework of graph-based learning, the key to robust subspace clustering and
subspace learning is to obtain a good similarity graph that eliminates the effects of errors …
subspace learning is to obtain a good similarity graph that eliminates the effects of errors …
Provable subspace clustering: When LRR meets SSC
Abstract Sparse Subspace Clustering (SSC) and Low-Rank Representation (LRR) are both
considered as the state-of-the-art methods for {\em subspace clustering}. The two methods …
considered as the state-of-the-art methods for {\em subspace clustering}. The two methods …