5 research outputs found
Twin Learning for Similarity and Clustering: A Unified Kernel Approach
Many similarity-based clustering methods work in two separate steps including
similarity matrix computation and subsequent spectral clustering. However,
similarity measurement is challenging because it is usually impacted by many
factors, e.g., the choice of similarity metric, neighborhood size, scale of
data, noise and outliers. Thus the learned similarity matrix is often not
suitable, let alone optimal, for the subsequent clustering. In addition,
nonlinear similarity often exists in many real world data which, however, has
not been effectively considered by most existing methods. To tackle these two
challenges, we propose a model to simultaneously learn cluster indicator matrix
and similarity information in kernel spaces in a principled way. We show
theoretical relationships to kernel k-means, k-means, and spectral clustering
methods. Then, to address the practical issue of how to select the most
suitable kernel for a particular clustering task, we further extend our model
with a multiple kernel learning ability. With this joint model, we can
automatically accomplish three subtasks of finding the best cluster indicator
matrix, the most accurate similarity relations and the optimal combination of
multiple kernels. By leveraging the interactions between these three subtasks
in a joint framework, each subtask can be iteratively boosted by using the
results of the others towards an overall optimal solution. Extensive
experiments are performed to demonstrate the effectiveness of our method.Comment: Published in AAAI 201
Convex Subspace Clustering by Adaptive Block Diagonal Representation
Subspace clustering is a class of extensively studied clustering methods and
the spectral-type approaches are its important subclass whose key first step is
to learn a coefficient matrix with block diagonal structure. To realize this
step, sparse subspace clustering (SSC), low rank representation (LRR) and block
diagonal representation (BDR) were successively proposed and have become the
state-of-the-arts (SOTAs). Among them, the former two minimize their convex
objectives by imposing sparsity and low rankness on the coefficient matrix
respectively, but so-desired block diagonality cannot neccesarily be guaranteed
practically while the latter designs a block diagonal matrix induced
regularizer but sacrifices convexity. For solving this dilemma, inspired by
Convex Biclustering, in this paper, we propose a simple yet efficient
spectral-type subspace clustering method named Adaptive Block Diagonal
Representation (ABDR) which strives to pursue so-desired block diagonality as
BDR by coercively fusing the columns/rows of the coefficient matrix via a
specially designed convex regularizer, consequently, ABDR naturally enjoys
their merits and can adaptively form more desired block diagonality than the
SOTAs without needing to prefix the number of blocks as done in BDR. Finally,
experimental results on synthetic and real benchmarks demonstrate the
superiority of ABDR.Comment: 13 pages, 11 figures, 8 table