4,288 research outputs found
Revisiting Kernelized Locality-Sensitive Hashing for Improved Large-Scale Image Retrieval
We present a simple but powerful reinterpretation of kernelized
locality-sensitive hashing (KLSH), a general and popular method developed in
the vision community for performing approximate nearest-neighbor searches in an
arbitrary reproducing kernel Hilbert space (RKHS). Our new perspective is based
on viewing the steps of the KLSH algorithm in an appropriately projected space,
and has several key theoretical and practical benefits. First, it eliminates
the problematic conceptual difficulties that are present in the existing
motivation of KLSH. Second, it yields the first formal retrieval performance
bounds for KLSH. Third, our analysis reveals two techniques for boosting the
empirical performance of KLSH. We evaluate these extensions on several
large-scale benchmark image retrieval data sets, and show that our analysis
leads to improved recall performance of at least 12%, and sometimes much
higher, over the standard KLSH method.Comment: 15 page
Kernel methods for detecting coherent structures in dynamical data
We illustrate relationships between classical kernel-based dimensionality
reduction techniques and eigendecompositions of empirical estimates of
reproducing kernel Hilbert space (RKHS) operators associated with dynamical
systems. In particular, we show that kernel canonical correlation analysis
(CCA) can be interpreted in terms of kernel transfer operators and that it can
be obtained by optimizing the variational approach for Markov processes (VAMP)
score. As a result, we show that coherent sets of particle trajectories can be
computed by kernel CCA. We demonstrate the efficiency of this approach with
several examples, namely the well-known Bickley jet, ocean drifter data, and a
molecular dynamics problem with a time-dependent potential. Finally, we propose
a straightforward generalization of dynamic mode decomposition (DMD) called
coherent mode decomposition (CMD). Our results provide a generic machine
learning approach to the computation of coherent sets with an objective score
that can be used for cross-validation and the comparison of different methods
Riemannian kernel based Nystr\"om method for approximate infinite-dimensional covariance descriptors with application to image set classification
In the domain of pattern recognition, using the CovDs (Covariance
Descriptors) to represent data and taking the metrics of the resulting
Riemannian manifold into account have been widely adopted for the task of image
set classification. Recently, it has been proven that infinite-dimensional
CovDs are more discriminative than their low-dimensional counterparts. However,
the form of infinite-dimensional CovDs is implicit and the computational load
is high. We propose a novel framework for representing image sets by
approximating infinite-dimensional CovDs in the paradigm of the Nystr\"om
method based on a Riemannian kernel. We start by modeling the images via CovDs,
which lie on the Riemannian manifold spanned by SPD (Symmetric Positive
Definite) matrices. We then extend the Nystr\"om method to the SPD manifold and
obtain the approximations of CovDs in RKHS (Reproducing Kernel Hilbert Space).
Finally, we approximate infinite-dimensional CovDs via these approximations.
Empirically, we apply our framework to the task of image set classification.
The experimental results obtained on three benchmark datasets show that our
proposed approximate infinite-dimensional CovDs outperform the original CovDs.Comment: 6 pages, 3 figures, International Conference on Pattern Recognition
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Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces
Transfer operators such as the Perron--Frobenius or Koopman operator play an
important role in the global analysis of complex dynamical systems. The
eigenfunctions of these operators can be used to detect metastable sets, to
project the dynamics onto the dominant slow processes, or to separate
superimposed signals. We extend transfer operator theory to reproducing kernel
Hilbert spaces and show that these operators are related to Hilbert space
representations of conditional distributions, known as conditional mean
embeddings in the machine learning community. Moreover, numerical methods to
compute empirical estimates of these embeddings are akin to data-driven methods
for the approximation of transfer operators such as extended dynamic mode
decomposition and its variants. One main benefit of the presented kernel-based
approaches is that these methods can be applied to any domain where a
similarity measure given by a kernel is available. We illustrate the results
with the aid of guiding examples and highlight potential applications in
molecular dynamics as well as video and text data analysis
When Kernel Methods meet Feature Learning: Log-Covariance Network for Action Recognition from Skeletal Data
Human action recognition from skeletal data is a hot research topic and
important in many open domain applications of computer vision, thanks to
recently introduced 3D sensors. In the literature, naive methods simply
transfer off-the-shelf techniques from video to the skeletal representation.
However, the current state-of-the-art is contended between to different
paradigms: kernel-based methods and feature learning with (recurrent) neural
networks. Both approaches show strong performances, yet they exhibit heavy, but
complementary, drawbacks. Motivated by this fact, our work aims at combining
together the best of the two paradigms, by proposing an approach where a
shallow network is fed with a covariance representation. Our intuition is that,
as long as the dynamics is effectively modeled, there is no need for the
classification network to be deep nor recurrent in order to score favorably. We
validate this hypothesis in a broad experimental analysis over 6 publicly
available datasets.Comment: 2017 IEEE Computer Vision and Pattern Recognition (CVPR) Workshop
A Primer on Reproducing Kernel Hilbert Spaces
Reproducing kernel Hilbert spaces are elucidated without assuming prior
familiarity with Hilbert spaces. Compared with extant pedagogic material,
greater care is placed on motivating the definition of reproducing kernel
Hilbert spaces and explaining when and why these spaces are efficacious. The
novel viewpoint is that reproducing kernel Hilbert space theory studies
extrinsic geometry, associating with each geometric configuration a canonical
overdetermined coordinate system. This coordinate system varies continuously
with changing geometric configurations, making it well-suited for studying
problems whose solutions also vary continuously with changing geometry. This
primer can also serve as an introduction to infinite-dimensional linear algebra
because reproducing kernel Hilbert spaces have more properties in common with
Euclidean spaces than do more general Hilbert spaces.Comment: Revised version submitted to Foundations and Trends in Signal
Processin
Approximate Kernel PCA Using Random Features: Computational vs. Statistical Trade-off
Kernel methods are powerful learning methodologies that provide a simple way
to construct nonlinear algorithms from linear ones. Despite their popularity,
they suffer from poor scalability in big data scenarios. Various approximation
methods, including random feature approximation have been proposed to alleviate
the problem. However, the statistical consistency of most of these approximate
kernel methods is not well understood except for kernel ridge regression
wherein it has been shown that the random feature approximation is not only
computationally efficient but also statistically consistent with a minimax
optimal rate of convergence. In this paper, we investigate the efficacy of
random feature approximation in the context of kernel principal component
analysis (KPCA) by studying the trade-off between computational and statistical
behaviors of approximate KPCA. We show that the approximate KPCA is both
computationally and statistically efficient compared to KPCA in terms of the
error associated with reconstructing a kernel function based on its projection
onto the corresponding eigenspaces. Depending on the eigenvalue decay behavior
of the covariance operator, we show that only features (polynomial
decay) or features (exponential decay) are needed to match the
statistical performance of KPCA. We also investigate their statistical
behaviors in terms of the convergence of corresponding eigenspaces wherein we
show that only features are required to match the performance of
KPCA and if fewer than features are used, then approximate KPCA has
a worse statistical behavior than that of KPCA.Comment: 46 page
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