138 research outputs found
Spectral Clustering with Jensen-type kernels and their multi-point extensions
Motivated by multi-distribution divergences, which originate in information
theory, we propose a notion of `multi-point' kernels, and study their
applications. We study a class of kernels based on Jensen type divergences and
show that these can be extended to measure similarity among multiple points. We
study tensor flattening methods and develop a multi-point (kernel) spectral
clustering (MSC) method. We further emphasize on a special case of the proposed
kernels, which is a multi-point extension of the linear (dot-product) kernel
and show the existence of cubic time tensor flattening algorithm in this case.
Finally, we illustrate the usefulness of our contributions using standard data
sets and image segmentation tasks.Comment: To appear in IEEE Computer Society Conference on Computer Vision and
Pattern Recognitio
Information Theoretical Estimators Toolbox
We present ITE (information theoretical estimators) a free and open source,
multi-platform, Matlab/Octave toolbox that is capable of estimating many
different variants of entropy, mutual information, divergence, association
measures, cross quantities, and kernels on distributions. Thanks to its highly
modular design, ITE supports additionally (i) the combinations of the
estimation techniques, (ii) the easy construction and embedding of novel
information theoretical estimators, and (iii) their immediate application in
information theoretical optimization problems. ITE also includes a prototype
application in a central problem class of signal processing, independent
subspace analysis and its extensions.Comment: 5 pages; ITE toolbox: https://bitbucket.org/szzoli/ite
Properties of Classical and Quantum Jensen-Shannon Divergence
Jensen-Shannon divergence (JD) is a symmetrized and smoothed version of the
most important divergence measure of information theory, Kullback divergence.
As opposed to Kullback divergence it determines in a very direct way a metric;
indeed, it is the square of a metric. We consider a family of divergence
measures (JD_alpha for alpha>0), the Jensen divergences of order alpha, which
generalize JD as JD_1=JD. Using a result of Schoenberg, we prove that JD_alpha
is the square of a metric for alpha lies in the interval (0,2], and that the
resulting metric space of probability distributions can be isometrically
embedded in a real Hilbert space. Quantum Jensen-Shannon divergence (QJD) is a
symmetrized and smoothed version of quantum relative entropy and can be
extended to a family of quantum Jensen divergences of order alpha (QJD_alpha).
We strengthen results by Lamberti et al. by proving that for qubits and pure
states, QJD_alpha^1/2 is a metric space which can be isometrically embedded in
a real Hilbert space when alpha lies in the interval (0,2]. In analogy with
Burbea and Rao's generalization of JD, we also define general QJD by
associating a Jensen-type quantity to any weighted family of states.
Appropriate interpretations of quantities introduced are discussed and bounds
are derived in terms of the total variation and trace distance.Comment: 13 pages, LaTeX, expanded contents, added references and corrected
typo
Convergence rate of Tsallis entropic regularized optimal transport
In this paper, we consider Tsallis entropic regularized optimal transport and
discuss the convergence rate as the regularization parameter goes
to . In particular, we establish the convergence rate of the Tsallis
entropic regularized optimal transport using the quantization and shadow
arguments developed by Eckstein--Nutz. We compare this to the convergence rate
of the entropic regularized optimal transport with Kullback--Leibler (KL)
divergence and show that KL is the fastest convergence rate in terms of Tsallis
relative entropy.Comment: 21 page
Fast depth-based subgraph kernels for unattributed graphs
In this paper, we investigate two fast subgraph kernels based on a depth-based representation of graph-structure. Both methods gauge depth information through a family of K-layer expansion subgraphs rooted at a vertex [1]. The first method commences by computing a centroid-based complexity trace for each graph, using a depth-based representation rooted at the centroid vertex that has minimum shortest path length variance to the remaining vertices [2]. This subgraph kernel is computed by measuring the Jensen-Shannon divergence between centroid-based complexity entropy traces. The second method, on the other hand, computes a depth-based representation around each vertex in turn. The corresponding subgraph kernel is computed using isomorphisms tests to compare the depth-based representation rooted at each vertex in turn. For graphs with n vertices, the time complexities for the two new kernels are O(n 2) and O(n 3), in contrast to O(n 6) for the classic Gärtner graph kernel [3]. Key to achieving this efficiency is that we compute the required Shannon entropy of the random walk for our kernels with O(n 2) operations. This computational strategy enables our subgraph kernels to easily scale up to graphs of reasonably large sizes and thus overcome the size limits arising in state-of-the-art graph kernels. Experiments on standard bioinformatics and computer vision graph datasets demonstrate the effectiveness and efficiency of our new subgraph kernels
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