6,749 research outputs found
A Degeneracy Framework for Scalable Graph Autoencoders
In this paper, we present a general framework to scale graph autoencoders
(AE) and graph variational autoencoders (VAE). This framework leverages graph
degeneracy concepts to train models only from a dense subset of nodes instead
of using the entire graph. Together with a simple yet effective propagation
mechanism, our approach significantly improves scalability and training speed
while preserving performance. We evaluate and discuss our method on several
variants of existing graph AE and VAE, providing the first application of these
models to large graphs with up to millions of nodes and edges. We achieve
empirically competitive results w.r.t. several popular scalable node embedding
methods, which emphasizes the relevance of pursuing further research towards
more scalable graph AE and VAE.Comment: International Joint Conference on Artificial Intelligence (IJCAI
2019
Neural Collaborative Subspace Clustering
We introduce the Neural Collaborative Subspace Clustering, a neural model
that discovers clusters of data points drawn from a union of low-dimensional
subspaces. In contrast to previous attempts, our model runs without the aid of
spectral clustering. This makes our algorithm one of the kinds that can
gracefully scale to large datasets. At its heart, our neural model benefits
from a classifier which determines whether a pair of points lies on the same
subspace or not. Essential to our model is the construction of two affinity
matrices, one from the classifier and the other from a notion of subspace
self-expressiveness, to supervise training in a collaborative scheme. We
thoroughly assess and contrast the performance of our model against various
state-of-the-art clustering algorithms including deep subspace-based ones.Comment: Accepted to ICML 201
Efficient Semidefinite Spectral Clustering via Lagrange Duality
We propose an efficient approach to semidefinite spectral clustering (SSC),
which addresses the Frobenius normalization with the positive semidefinite
(p.s.d.) constraint for spectral clustering. Compared with the original
Frobenius norm approximation based algorithm, the proposed algorithm can more
accurately find the closest doubly stochastic approximation to the affinity
matrix by considering the p.s.d. constraint. In this paper, SSC is formulated
as a semidefinite programming (SDP) problem. In order to solve the high
computational complexity of SDP, we present a dual algorithm based on the
Lagrange dual formalization. Two versions of the proposed algorithm are
proffered: one with less memory usage and the other with faster convergence
rate. The proposed algorithm has much lower time complexity than that of the
standard interior-point based SDP solvers. Experimental results on both UCI
data sets and real-world image data sets demonstrate that 1) compared with the
state-of-the-art spectral clustering methods, the proposed algorithm achieves
better clustering performance; and 2) our algorithm is much more efficient and
can solve larger-scale SSC problems than those standard interior-point SDP
solvers.Comment: 13 page
- …