6,474 research outputs found
Approximation errors of online sparsification criteria
Many machine learning frameworks, such as resource-allocating networks,
kernel-based methods, Gaussian processes, and radial-basis-function networks,
require a sparsification scheme in order to address the online learning
paradigm. For this purpose, several online sparsification criteria have been
proposed to restrict the model definition on a subset of samples. The most
known criterion is the (linear) approximation criterion, which discards any
sample that can be well represented by the already contributing samples, an
operation with excessive computational complexity. Several computationally
efficient sparsification criteria have been introduced in the literature, such
as the distance, the coherence and the Babel criteria. In this paper, we
provide a framework that connects these sparsification criteria to the issue of
approximating samples, by deriving theoretical bounds on the approximation
errors. Moreover, we investigate the error of approximating any feature, by
proposing upper-bounds on the approximation error for each of the
aforementioned sparsification criteria. Two classes of features are described
in detail, the empirical mean and the principal axes in the kernel principal
component analysis.Comment: 10 page
Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data
Similarity-based approaches represent a promising direction for time series
analysis. However, many such methods rely on parameter tuning, and some have
shortcomings if the time series are multivariate (MTS), due to dependencies
between attributes, or the time series contain missing data. In this paper, we
address these challenges within the powerful context of kernel methods by
proposing the robust \emph{time series cluster kernel} (TCK). The approach
taken leverages the missing data handling properties of Gaussian mixture models
(GMM) augmented with informative prior distributions. An ensemble learning
approach is exploited to ensure robustness to parameters by combining the
clustering results of many GMM to form the final kernel.
We evaluate the TCK on synthetic and real data and compare to other
state-of-the-art techniques. The experimental results demonstrate that the TCK
is robust to parameter choices, provides competitive results for MTS without
missing data and outstanding results for missing data.Comment: 23 pages, 6 figure
Unsupervised Adaptive Re-identification in Open World Dynamic Camera Networks
Person re-identification is an open and challenging problem in computer
vision. Existing approaches have concentrated on either designing the best
feature representation or learning optimal matching metrics in a static setting
where the number of cameras are fixed in a network. Most approaches have
neglected the dynamic and open world nature of the re-identification problem,
where a new camera may be temporarily inserted into an existing system to get
additional information. To address such a novel and very practical problem, we
propose an unsupervised adaptation scheme for re-identification models in a
dynamic camera network. First, we formulate a domain perceptive
re-identification method based on geodesic flow kernel that can effectively
find the best source camera (already installed) to adapt with a newly
introduced target camera, without requiring a very expensive training phase.
Second, we introduce a transitive inference algorithm for re-identification
that can exploit the information from best source camera to improve the
accuracy across other camera pairs in a network of multiple cameras. Extensive
experiments on four benchmark datasets demonstrate that the proposed approach
significantly outperforms the state-of-the-art unsupervised learning based
alternatives whilst being extremely efficient to compute.Comment: CVPR 2017 Spotligh
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