6 research outputs found
Anomaly Detection with Selective Dictionary Learning
In this paper we present new methods of anomaly detection based on Dictionary
Learning (DL) and Kernel Dictionary Learning (KDL). The main contribution
consists in the adaption of known DL and KDL algorithms in the form of
unsupervised methods, used for outlier detection. We propose a reduced kernel
version (RKDL), which is useful for problems with large data sets, due to the
large kernel matrix. We also improve the DL and RKDL methods by the use of a
random selection of signals, which aims to eliminate the outliers from the
training procedure. All our algorithms are introduced in an anomaly detection
toolbox and are compared to standard benchmark results
Reduced Kernel Dictionary Learning
In this paper we present new algorithms for training reduced-size nonlinear
representations in the Kernel Dictionary Learning (KDL) problem. Standard KDL
has the drawback of a large size of the kernel matrix when the data set is
large. There are several ways of reducing the kernel size, notably Nystr\"om
sampling. We propose here a method more in the spirit of dictionary learning,
where the kernel vectors are obtained with a trained sparse representation of
the input signals. Moreover, we optimize directly the kernel vectors in the KDL
process, using gradient descent steps. We show with three data sets that our
algorithms are able to provide better representations, despite using a small
number of kernel vectors, and also decrease the execution time with respect to
KDL
Classification with Incoherent Kernel Dictionary Learning
In this paper we present a new classification method based on Dictionary
Learning (DL). The main contribution consists of a kernel version of incoherent
DL, derived from its standard linear counterpart. We also propose an
improvement of the AK-SVD algorithm concerning the representation update. Our
algorithms are tested on several popular databases of classification problems
Machine learning with neuroimaging data to identify autism spectrum disorder: a systematic review and meta-analysis
Purpose: Autism Spectrum Disorder (ASD) is diagnosed through observation or interview assessments, which is time-consuming, subjective, and with questionable validity and reliability. Thus, we aimed to evaluate the role of machine learning (ML) with neuroimaging data to provide a reliable classification of ASD. Methods: A systematic search of PubMed, Scopus, and Embase was conducted to identify relevant publications. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to assess the studies’ quality. A bivariate random-effects model meta-analysis was employed to evaluate the pooled sensitivity, the pooled specificity, and the diagnostic performance through the hierarchical summary receiver operating characteristic (HSROC) curve of ML with neuroimaging data in classifying ASD. Meta-regression was also performed. Results: Forty-four studies (5697 ASD and 6013 typically developing individuals [TD] in total) were included in the quantitative analysis. The pooled sensitivity for differentiating ASD from TD individuals was 86.25 95% confidence interval [CI] (81.24, 90.08), while the pooled specificity was 83.31 95% CI (78.12, 87.48) with a combined area under the HSROC (AUC) of 0.889. Higgins I2 (> 90%) and Cochran’s Q (p < 0.0001) suggest a high degree of heterogeneity. In the bivariate model meta-regression, a higher pooled specificity was observed in studies not using a brain atlas (90.91 95% CI [80.67, 96.00], p = 0.032). In addition, a greater pooled sensitivity was seen in studies recruiting both males and females (89.04 95% CI [83.84, 92.72], p = 0.021), and combining imaging modalities (94.12 95% [85.43, 97.76], p = 0.036). Conclusion: ML with neuroimaging data is an exciting prospect in detecting individuals with ASD but further studies are required to improve its reliability for usage in clinical practice
Kernel t-distributed stochastic neighbor embedding
This paper presents a kernelized version of the t-SNE algorithm, capable of
mapping high-dimensional data to a low-dimensional space while preserving the
pairwise distances between the data points in a non-Euclidean metric. This can
be achieved using a kernel trick only in the high dimensional space or in both
spaces, leading to an end-to-end kernelized version. The proposed kernelized
version of the t-SNE algorithm can offer new views on the relationships between
data points, which can improve performance and accuracy in particular
applications, such as classification problems involving kernel methods. The
differences between t-SNE and its kernelized version are illustrated for
several datasets, showing a neater clustering of points belonging to different
classes
Sparse Representation With Gaussian Atoms and Its Use in Anomaly Detection
We propose sparse representation and dictionary learning algorithms for dictionaries whose atoms are characterized by Gaussian probability distributions around some central atoms. This extension of the space covered by the atoms permits a better characterization of localized features of the signals. The representations are computed by trading-off representation error and the probability of the actual atoms used in the representation. We propose two types of algorithms: a greedy one, similar in spirit with Orthogonal Matching Pursuit, and one based on L1-regularization. We apply our new algorithms to unsupervised anomaly detection, where the representation error is used as anomaly score. The flexibility of our approach appears to improve more the representations of the many normal signals than those of the few outliers, at least for an anomaly type called dependency, thus improving the detection quality. Comparison with both standard dictionary learning algorithms and established anomaly detection methods is favorable