2 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
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