1,877 research outputs found
Metric learning pairwise kernel for graph inference
Much recent work in bioinformatics has focused on the inference of various
types of biological networks, representing gene regulation, metabolic
processes, protein-protein interactions, etc. A common setting involves
inferring network edges in a supervised fashion from a set of high-confidence
edges, possibly characterized by multiple, heterogeneous data sets (protein
sequence, gene expression, etc.). Here, we distinguish between two modes of
inference in this setting: direct inference based upon similarities between
nodes joined by an edge, and indirect inference based upon similarities between
one pair of nodes and another pair of nodes. We propose a supervised approach
for the direct case by translating it into a distance metric learning problem.
A relaxation of the resulting convex optimization problem leads to the support
vector machine (SVM) algorithm with a particular kernel for pairs, which we
call the metric learning pairwise kernel (MLPK). We demonstrate, using several
real biological networks, that this direct approach often improves upon the
state-of-the-art SVM for indirect inference with the tensor product pairwise
kernel
Recognition of 3-D Objects from Multiple 2-D Views by a Self-Organizing Neural Architecture
The recognition of 3-D objects from sequences of their 2-D views is modeled by a neural architecture, called VIEWNET that uses View Information Encoded With NETworks. VIEWNET illustrates how several types of noise and varialbility in image data can be progressively removed while incornplcte image features are restored and invariant features are discovered using an appropriately designed cascade of processing stages. VIEWNET first processes 2-D views of 3-D objects using the CORT-X 2 filter, which discounts the illuminant, regularizes and completes figural boundaries, and removes noise from the images. Boundary regularization and cornpletion are achieved by the same mechanisms that suppress image noise. A log-polar transform is taken with respect to the centroid of the resulting figure and then re-centered to achieve 2-D scale and rotation invariance. The invariant images are coarse coded to further reduce noise, reduce foreshortening effects, and increase generalization. These compressed codes are input into a supervised learning system based on the fuzzy ARTMAP algorithm. Recognition categories of 2-D views are learned before evidence from sequences of 2-D view categories is accumulated to improve object recognition. Recognition is studied with noisy and clean images using slow and fast learning. VIEWNET is demonstrated on an MIT Lincoln Laboratory database of 2-D views of jet aircraft with and without additive noise. A recognition rate of 90% is achieved with one 2-D view category and of 98.5% correct with three 2-D view categories.National Science Foundation (IRI 90-24877); Office of Naval Research (N00014-91-J-1309, N00014-91-J-4100, N00014-92-J-0499); Air Force Office of Scientific Research (F9620-92-J-0499, 90-0083
Graph kernels between point clouds
Point clouds are sets of points in two or three dimensions. Most kernel
methods for learning on sets of points have not yet dealt with the specific
geometrical invariances and practical constraints associated with point clouds
in computer vision and graphics. In this paper, we present extensions of graph
kernels for point clouds, which allow to use kernel methods for such ob jects
as shapes, line drawings, or any three-dimensional point clouds. In order to
design rich and numerically efficient kernels with as few free parameters as
possible, we use kernels between covariance matrices and their factorizations
on graphical models. We derive polynomial time dynamic programming recursions
and present applications to recognition of handwritten digits and Chinese
characters from few training examples
The Weight Function in the Subtree Kernel is Decisive
Tree data are ubiquitous because they model a large variety of situations,
e.g., the architecture of plants, the secondary structure of RNA, or the
hierarchy of XML files. Nevertheless, the analysis of these non-Euclidean data
is difficult per se. In this paper, we focus on the subtree kernel that is a
convolution kernel for tree data introduced by Vishwanathan and Smola in the
early 2000's. More precisely, we investigate the influence of the weight
function from a theoretical perspective and in real data applications. We
establish on a 2-classes stochastic model that the performance of the subtree
kernel is improved when the weight of leaves vanishes, which motivates the
definition of a new weight function, learned from the data and not fixed by the
user as usually done. To this end, we define a unified framework for computing
the subtree kernel from ordered or unordered trees, that is particularly
suitable for tuning parameters. We show through eight real data classification
problems the great efficiency of our approach, in particular for small
datasets, which also states the high importance of the weight function.
Finally, a visualization tool of the significant features is derived.Comment: 36 page
Tree Edit Distance Learning via Adaptive Symbol Embeddings
Metric learning has the aim to improve classification accuracy by learning a
distance measure which brings data points from the same class closer together
and pushes data points from different classes further apart. Recent research
has demonstrated that metric learning approaches can also be applied to trees,
such as molecular structures, abstract syntax trees of computer programs, or
syntax trees of natural language, by learning the cost function of an edit
distance, i.e. the costs of replacing, deleting, or inserting nodes in a tree.
However, learning such costs directly may yield an edit distance which violates
metric axioms, is challenging to interpret, and may not generalize well. In
this contribution, we propose a novel metric learning approach for trees which
we call embedding edit distance learning (BEDL) and which learns an edit
distance indirectly by embedding the tree nodes as vectors, such that the
Euclidean distance between those vectors supports class discrimination. We
learn such embeddings by reducing the distance to prototypical trees from the
same class and increasing the distance to prototypical trees from different
classes. In our experiments, we show that BEDL improves upon the
state-of-the-art in metric learning for trees on six benchmark data sets,
ranging from computer science over biomedical data to a natural-language
processing data set containing over 300,000 nodes.Comment: Paper at the International Conference of Machine Learning (2018),
2018-07-10 to 2018-07-15 in Stockholm, Swede
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