5,370 research outputs found
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features
One-class support vector machine (OC-SVM) for a long time has been one of the
most effective anomaly detection methods and extensively adopted in both
research as well as industrial applications. The biggest issue for OC-SVM is
yet the capability to operate with large and high-dimensional datasets due to
optimization complexity. Those problems might be mitigated via dimensionality
reduction techniques such as manifold learning or autoencoder. However,
previous work often treats representation learning and anomaly prediction
separately. In this paper, we propose autoencoder based one-class support
vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier
features to approximate the radial basis kernel, into deep learning context by
combining it with a representation learning architecture and jointly exploit
stochastic gradient descent to obtain end-to-end training. Interestingly, this
also opens up the possible use of gradient-based attribution methods to explain
the decision making for anomaly detection, which has ever been challenging as a
result of the implicit mappings between the input space and the kernel space.
To the best of our knowledge, this is the first work to study the
interpretability of deep learning in anomaly detection. We evaluate our method
on a wide range of unsupervised anomaly detection tasks in which our end-to-end
training architecture achieves a performance significantly better than the
previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201
On the role of pre and post-processing in environmental data mining
The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed
Shepherding Slots to Objects: Towards Stable and Robust Object-Centric Learning
Object-centric learning (OCL) aspires general and compositional understanding
of scenes by representing a scene as a collection of object-centric
representations. OCL has also been extended to multi-view image and video
datasets to apply various data-driven inductive biases by utilizing geometric
or temporal information in the multi-image data. Single-view images carry less
information about how to disentangle a given scene than videos or multi-view
images do. Hence, owing to the difficulty of applying inductive biases, OCL for
single-view images remains challenging, resulting in inconsistent learning of
object-centric representation. To this end, we introduce a novel OCL framework
for single-view images, SLot Attention via SHepherding (SLASH), which consists
of two simple-yet-effective modules on top of Slot Attention. The new modules,
Attention Refining Kernel (ARK) and Intermediate Point Predictor and Encoder
(IPPE), respectively, prevent slots from being distracted by the background
noise and indicate locations for slots to focus on to facilitate learning of
object-centric representation. We also propose a weak semi-supervision approach
for OCL, whilst our proposed framework can be used without any assistant
annotation during the inference. Experiments show that our proposed method
enables consistent learning of object-centric representation and achieves
strong performance across four datasets. Code is available at
\url{https://github.com/object-understanding/SLASH}
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