Model-based subspace clustering
نویسنده
چکیده
We discuss a model-based approach to identifying clusters of objects based on subsets of attributes, so that the attributes that distinguish a cluster from the rest of the population may depend on the cluster being considered. The method is based on a Pólya urn cluster model for multivariate means and variances, resulting in a multivariate Dirichlet process mixture model. This particular model-based approach accommodates outliers and allows for the incorporation of application-specific data features into the clustering scheme. For example, in an analysis of genetic CGH array data we are able to design a clustering method that accounts for spatial dependence of chromosomal abnormalities.
منابع مشابه
ASCLU: Alternative Subspace Clustering
Finding groups of similar objects in databases is one of the most important data mining tasks. Recently, traditional clustering approaches have been extended to generate alternative clustering solutions. The basic observation is that for each database object multiple meaningful groupings might exist: the data allows to be clustered through different perspectives. It is thus reasonable to search...
متن کاملDeep Subspace Clustering with Sparsity Prior
Subspace clustering aims to cluster unlabeled samples into multiple groups by implicitly seeking a subspace to fit each group. Most of existing methods are based on a shallow linear model, which may fail in handling data with nonlinear structure. In this paper, we propose a novel subspace clustering method – deeP subspAce clusteRing with sparsiTY prior (PARTY) – based on a new deep learning arc...
متن کاملFast Subspace Clustering Based on the Kronecker Product
Subspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is often smaller than the ambient dimension. Spectral clustering, as one of the main approaches to subspace clustering, often takes on a sparse representation or a low-rank representation to learn a block diagonal self-representation matrix for subspace gener...
متن کاملSubspace Clustering Reloaded: Sparse vs. Dense Representations
State-of-the-art methods for learning unions of subspaces from a collection of data leverage sparsity to form representations of each vector in the dataset with respect to the remaining vectors in the dataset. The resulting sparse representations can be used to form a subspace affinity matrix to cluster the data into their respective subspaces. While sparsity-driven methods for subspace cluster...
متن کاملLearning Markov random walks for robust subspace clustering and estimation
Markov Random Walks (MRW) has proven to be an effective way to understand spectral clustering and embedding. However, due to less global structural measure, conventional MRW (e.g., the Gaussian kernel MRW) cannot be applied to handle data points drawn from a mixture of subspaces. In this paper, we introduce a regularized MRW learning model, using a low-rank penalty to constrain the global subsp...
متن کاملGroupwise Constrained Reconstruction for Subspace Clustering
Reconstruction based subspace clustering methods compute a self reconstruction matrix over the samples and use it for spectral clustering to obtain the final clustering result. Their success largely relies on the assumption that the underlying subspaces are independent, which, however, does not always hold in the applications with increasing number of subspaces. In this paper, we propose a nove...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2005