985 research outputs found
Targeted collapse regularized autoencoder for anomaly detection: black hole at the center
Autoencoders have been extensively used in the development of recent anomaly
detection techniques. The premise of their application is based on the notion
that after training the autoencoder on normal training data, anomalous inputs
will exhibit a significant reconstruction error. Consequently, this enables a
clear differentiation between normal and anomalous samples. In practice,
however, it is observed that autoencoders can generalize beyond the normal
class and achieve a small reconstruction error on some of the anomalous
samples. To improve the performance, various techniques propose additional
components and more sophisticated training procedures. In this work, we propose
a remarkably straightforward alternative: instead of adding neural network
components, involved computations, and cumbersome training, we complement the
reconstruction loss with a computationally light term that regulates the norm
of representations in the latent space. The simplicity of our approach
minimizes the requirement for hyperparameter tuning and customization for new
applications which, paired with its permissive data modality constraint,
enhances the potential for successful adoption across a broad range of
applications. We test the method on various visual and tabular benchmarks and
demonstrate that the technique matches and frequently outperforms alternatives.
We also provide a theoretical analysis and numerical simulations that help
demonstrate the underlying process that unfolds during training and how it can
help with anomaly detection. This mitigates the black-box nature of
autoencoder-based anomaly detection algorithms and offers an avenue for further
investigation of advantages, fail cases, and potential new directions.Comment: 16 pages, 4 figures, 4 table
Robust and Adversarial Data Mining
In the domain of data mining and machine learning, researchers have made significant contributions in developing algorithms handling clustering and classification problems. We develop algorithms under assumptions that are not met by previous works. (i) In adversarial learning, which is the study of machine learning techniques deployed in non-benign environments. We design an algorithm to show how a classifier should be designed to be robust against sparse adversarial attacks. Our main insight is that sparse feature attacks are best defended by designing classifiers which use L1 regularizers. (ii) The different properties between L1 (Lasso) and L2 (Tikhonov or Ridge) regularization has been studied extensively. However, given a data set, principle to follow in terms of choosing the suitable regularizer is yet to be developed. We use mathematical properties of the two regularization methods followed by detailed experimentation to understand their impact based on four characteristics. (iii) The identification of anomalies is an inherent component of knowledge discovery. In lots of cases, the number of features of a data set can be traced to a much smaller set of features. We claim that algorithms applied in a latent space are more robust. This can lead to more accurate results, and potentially provide a natural medium to explain and describe outliers. (iv) We also apply data mining techniques on health care industry. In a lot cases, health insurance companies cover unnecessary costs carried out by healthcare providers. The potential adversarial behaviours of surgeon physicians are addressed. We describe a specific con- text of private healthcare in Australia and describe our social network based approach (applied to health insurance claims) to understand the nature of collaboration among doctors treating hospital inpatients and explore the impact of collaboration on cost and quality of care. (v) We further develop models that predict the behaviours of orthopaedic surgeons in regard to surgery type and use of prosthetic device. An important feature of these models is that they can not only predict the behaviours of surgeons but also provide explanation for the predictions
Spatiotemporal Tensor Completion for Improved Urban Traffic Imputation
Effective management of urban traffic is important for any smart city
initiative. Therefore, the quality of the sensory traffic data is of paramount
importance. However, like any sensory data, urban traffic data are prone to
imperfections leading to missing measurements. In this paper, we focus on
inter-region traffic data completion. We model the inter-region traffic as a
spatiotemporal tensor that suffers from missing measurements. To recover the
missing data, we propose an enhanced CANDECOMP/PARAFAC (CP) completion approach
that considers the urban and temporal aspects of the traffic. To derive the
urban characteristics, we divide the area of study into regions. Then, for each
region, we compute urban feature vectors inspired from biodiversity which are
used to compute the urban similarity matrix. To mine the temporal aspect, we
first conduct an entropy analysis to determine the most regular time-series.
Then, we conduct a joint Fourier and correlation analysis to compute its
periodicity and construct the temporal matrix. Both urban and temporal matrices
are fed into a modified CP-completion objective function. To solve this
objective, we propose an alternating least square approach that operates on the
vectorized version of the inputs. We conduct comprehensive comparative study
with two evaluation scenarios. In the first one, we simulate random missing
values. In the second scenario, we simulate missing values at a given area and
time duration. Our results demonstrate that our approach provides effective
recovering performance reaching 26% improvement compared to state-of-art CP
approaches and 35% compared to state-of-art generative model-based approaches
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