92 research outputs found
A data-driven approach for Network Intrusion Detection and Monitoring based on Kernel Null Space
International audienceIn this study, we propose a new approach to determine intrusions of network in real-time based on statistical process control technique and kernel null space method. The training samples in a class are mapped to a single point using the Kernel Null Foley-Sammon Transform. The Novelty Score are computed from testing samples in order to determine the threshold for the real-time detection of anomaly. The efficiency of the proposed method is illustrated over the KDD99 data set. The experimental results show that our new method outperforms the OCSVM and the original Kernel Null Space method by 1.53% and 3.86% respectively in terms of accuracy
Learning a Discriminative Null Space for Person Re-identification
Most existing person re-identification (re-id) methods focus on learning the
optimal distance metrics across camera views. Typically a person's appearance
is represented using features of thousands of dimensions, whilst only hundreds
of training samples are available due to the difficulties in collecting matched
training images. With the number of training samples much smaller than the
feature dimension, the existing methods thus face the classic small sample size
(SSS) problem and have to resort to dimensionality reduction techniques and/or
matrix regularisation, which lead to loss of discriminative power. In this
work, we propose to overcome the SSS problem in re-id distance metric learning
by matching people in a discriminative null space of the training data. In this
null space, images of the same person are collapsed into a single point thus
minimising the within-class scatter to the extreme and maximising the relative
between-class separation simultaneously. Importantly, it has a fixed dimension,
a closed-form solution and is very efficient to compute. Extensive experiments
carried out on five person re-identification benchmarks including VIPeR,
PRID2011, CUHK01, CUHK03 and Market1501 show that such a simple approach beats
the state-of-the-art alternatives, often by a big margin.Comment: accepted by CVPR201
An improved LDA approach
2004-2005 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe
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