Subspace Clustering by Block Diagonal Representation
نویسندگان
چکیده
منابع مشابه
A Generative Block-Diagonal Model for Clustering
Probabilistic mixture models are among the most important clustering methods. These models assume that the feature vectors of the samples can be described by a mixture of several components. Each of these components follows a distribution of a certain form. In recent years, there has been an increasing amount of interest and work in similarity-matrix-based methods. Rather than considering the f...
متن کاملSymmetric low-rank representation for subspace clustering
We propose a symmetric low-rank representation (SLRR) method for subspace clustering, which assumes that a data set is approximately drawn from the union of multiple subspaces. The proposed technique can reveal the membership of multiple subspaces through the self-expressiveness property of the data. In particular, the SLRR method considers a collaborative representation combined with low-rank ...
متن کاملDiscriminative Block-Diagonal Representation Learning for Image Recognition
Existing block-diagonal representation studies mainly focuses on casting block-diagonal regularization on training data, while only little attention is dedicated to concurrently learning both block-diagonal representations of training and test data. In this paper, we propose a discriminative block-diagonal low-rank representation (BDLRR) method for recognition. In particular, the elaborate BDLR...
متن کاملBlock Diagonal Majorization on $C_{0}$
Let $mathbf{c}_0$ be the real vector space of all real sequences which converge to zero. For every $x,yin mathbf{c}_0$, it is said that $y$ is block diagonal majorized by $x$ (written $yprec_b x$) if there exists a block diagonal row stochastic matrix $R$ such that $y=Rx$. In this paper we find the possible structure of linear functions $T:mathbf{c}_0rightarrow mathbf{c}_0$ preserving $prec_b$.
متن کاملClustering Patient Medical Records via Sparse Subspace Representation
The health industry is facing increasing challenge with “big data” as traditional methods fail to manage the scale and complexity. This paper examines clustering of patient records for chronic diseases to facilitate a better construction of care plans. We solve this problem under the framework of subspace clustering. Our novel contribution lies in the exploitation of sparse representation to di...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2019
ISSN: 0162-8828,2160-9292,1939-3539
DOI: 10.1109/tpami.2018.2794348