26 research outputs found
Self-Discriminative Modeling for Anomalous Graph Detection
This paper studies the problem of detecting anomalous graphs using a machine
learning model trained on only normal graphs, which has many applications in
molecule, biology, and social network data analysis. We present a
self-discriminative modeling framework for anomalous graph detection. The key
idea, mathematically and numerically illustrated, is to learn a discriminator
(classifier) from the given normal graphs together with pseudo-anomalous graphs
generated by a model jointly trained, where we never use any true anomalous
graphs and we hope that the generated pseudo-anomalous graphs interpolate
between normal ones and (real) anomalous ones. Under the framework, we provide
three algorithms with different computational efficiencies and stabilities for
anomalous graph detection. The three algorithms are compared with several
state-of-the-art graph-level anomaly detection baselines on nine popular graph
datasets (four with small size and five with moderate size) and show
significant improvement in terms of AUC. The success of our algorithms stems
from the integration of the discriminative classifier and the well-posed
pseudo-anomalous graphs, which provide new insights for anomaly detection.
Moreover, we investigate our algorithms for large-scale imbalanced graph
datasets. Surprisingly, our algorithms, though fully unsupervised, are able to
significantly outperform supervised learning algorithms of anomalous graph
detection. The corresponding reason is also analyzed.Comment: This work was submitted to NeurIPS 2023 but was unfortunately
rejecte
Restricted Generative Projection for One-Class Classification and Anomaly Detection
We present a simple framework for one-class classification and anomaly
detection. The core idea is to learn a mapping to transform the unknown
distribution of training (normal) data to a known target distribution.
Crucially, the target distribution should be sufficiently simple, compact, and
informative. The simplicity is to ensure that we can sample from the
distribution easily, the compactness is to ensure that the decision boundary
between normal data and abnormal data is clear and reliable, and the
informativeness is to ensure that the transformed data preserve the important
information of the original data. Therefore, we propose to use truncated
Gaussian, uniform in hypersphere, uniform on hypersphere, or uniform between
hyperspheres, as the target distribution. We then minimize the distance between
the transformed data distribution and the target distribution while keeping the
reconstruction error for the original data small enough. Comparative studies on
multiple benchmark datasets verify the effectiveness of our methods in
comparison to baselines
Low-Rank Tensor Recovery with Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization
The nuclear norm and Schatten- quasi-norm of a matrix are popular rank
proxies in low-rank matrix recovery. Unfortunately, computing the nuclear norm
or Schatten- quasi-norm of a tensor is NP-hard, which is a pity for low-rank
tensor completion (LRTC) and tensor robust principal component analysis
(TRPCA). In this paper, we propose a new class of rank regularizers based on
the Euclidean norms of the CP component vectors of a tensor and show that these
regularizers are monotonic transformations of tensor Schatten- quasi-norm.
This connection enables us to minimize the Schatten- quasi-norm in LRTC and
TRPCA implicitly. The methods do not use the singular value decomposition and
hence scale to big tensors. Moreover, the methods are not sensitive to the
choice of initial rank and provide an arbitrarily sharper rank proxy for
low-rank tensor recovery compared to nuclear norm. We provide theoretical
guarantees in terms of recovery error for LRTC and TRPCA, which show relatively
smaller of Schatten- quasi-norm leads to tighter error bounds.
Experiments using LRTC and TRPCA on synthetic data and natural images verify
the effectiveness and superiority of our methods compared to baseline methods
Centrality Graph Convolutional Networks for Skeleton-based Action Recognition
The topological structure of skeleton data plays a significant role in human
action recognition. Combining the topological structure with graph
convolutional networks has achieved remarkable performance. In existing
methods, modeling the topological structure of skeleton data only considered
the connections between the joints and bones, and directly use physical
information. However, there exists an unknown problem to investigate the key
joints, bones and body parts in every human action. In this paper, we propose
the centrality graph convolutional networks to uncover the overlooked
topological information, and best take advantage of the information to
distinguish key joints, bones, and body parts. A novel centrality graph
convolutional network firstly highlights the effects of the key joints and
bones to bring a definite improvement. Besides, the topological information of
the skeleton sequence is explored and combined to further enhance the
performance in a four-channel framework. Moreover, the reconstructed graph is
implemented by the adaptive methods on the training process, which further
yields improvements. Our model is validated by two large-scale datasets,
NTU-RGB+D and Kinetics, and outperforms the state-of-the-art methods