55,019 research outputs found
Lifelong Metric Learning
The state-of-the-art online learning approaches are only capable of learning
the metric for predefined tasks. In this paper, we consider lifelong learning
problem to mimic "human learning", i.e., endowing a new capability to the
learned metric for a new task from new online samples and incorporating
previous experiences and knowledge. Therefore, we propose a new metric learning
framework: lifelong metric learning (LML), which only utilizes the data of the
new task to train the metric model while preserving the original capabilities.
More specifically, the proposed LML maintains a common subspace for all learned
metrics, named lifelong dictionary, transfers knowledge from the common
subspace to each new metric task with task-specific idiosyncrasy, and redefines
the common subspace over time to maximize performance across all metric tasks.
For model optimization, we apply online passive aggressive optimization
algorithm to solve the proposed LML framework, where the lifelong dictionary
and task-specific partition are optimized alternatively and consecutively.
Finally, we evaluate our approach by analyzing several multi-task metric
learning datasets. Extensive experimental results demonstrate effectiveness and
efficiency of the proposed framework.Comment: 10 pages, 6 figure
Metric and Kernel Learning using a Linear Transformation
Metric and kernel learning are important in several machine learning
applications. However, most existing metric learning algorithms are limited to
learning metrics over low-dimensional data, while existing kernel learning
algorithms are often limited to the transductive setting and do not generalize
to new data points. In this paper, we study metric learning as a problem of
learning a linear transformation of the input data. We show that for
high-dimensional data, a particular framework for learning a linear
transformation of the data based on the LogDet divergence can be efficiently
kernelized to learn a metric (or equivalently, a kernel function) over an
arbitrarily high dimensional space. We further demonstrate that a wide class of
convex loss functions for learning linear transformations can similarly be
kernelized, thereby considerably expanding the potential applications of metric
learning. We demonstrate our learning approach by applying it to large-scale
real world problems in computer vision and text mining
Active Metric Learning from Relative Comparisons
This work focuses on active learning of distance metrics from relative
comparison information. A relative comparison specifies, for a data point
triplet , that instance is more similar to than to
. Such constraints, when available, have been shown to be useful toward
defining appropriate distance metrics. In real-world applications, acquiring
constraints often require considerable human effort. This motivates us to study
how to select and query the most useful relative comparisons to achieve
effective metric learning with minimum user effort. Given an underlying class
concept that is employed by the user to provide such constraints, we present an
information-theoretic criterion that selects the triplet whose answer leads to
the highest expected gain in information about the classes of a set of
examples. Directly applying the proposed criterion requires examining
triplets with instances, which is prohibitive even for datasets of moderate
size. We show that a randomized selection strategy can be used to reduce the
selection pool from to , allowing us to scale up to larger-size
problems. Experiments show that the proposed method consistently outperforms
two baseline policies
Graph R-CNN for Scene Graph Generation
We propose a novel scene graph generation model called Graph R-CNN, that is
both effective and efficient at detecting objects and their relations in
images. Our model contains a Relation Proposal Network (RePN) that efficiently
deals with the quadratic number of potential relations between objects in an
image. We also propose an attentional Graph Convolutional Network (aGCN) that
effectively captures contextual information between objects and relations.
Finally, we introduce a new evaluation metric that is more holistic and
realistic than existing metrics. We report state-of-the-art performance on
scene graph generation as evaluated using both existing and our proposed
metrics.Comment: 16 pages, ECCV 2018 camera read
On Clustering on Graphs with Multiple Edge Types
We study clustering on graphs with multiple edge types. Our main motivation
is that similarities between objects can be measured in many different metrics.
For instance similarity between two papers can be based on common authors,
where they are published, keyword similarity, citations, etc. As such, graphs
with multiple edges is a more accurate model to describe similarities between
objects. Each edge/metric provides only partial information about the data;
recovering full information requires aggregation of all the similarity metrics.
Clustering becomes much more challenging in this context, since in addition to
the difficulties of the traditional clustering problem, we have to deal with a
space of clusterings. We generalize the concept of clustering in single-edge
graphs to multi-edged graphs and investigate problems such as: Can we find a
clustering that remains good, even if we change the relative weights of
metrics? How can we describe the space of clusterings efficiently? Can we find
unexpected clusterings (a good clustering that is distant from all given
clusterings)? If given the ground-truth clustering, can we recover how the
weights for edge types were aggregated? %In this paper, we discuss these
problems and the underlying algorithmic challenges and propose some solutions.
We also present two case studies: one based on papers on Arxiv and one based on
CIA World Factbook
Creating Scalable and Interactive Web Applications Using High Performance Latent Variable Models
In this project we outline a modularized, scalable system for comparing
Amazon products in an interactive and informative way using efficient latent
variable models and dynamic visualization. We demonstrate how our system can
build on the structure and rich review information of Amazon products in order
to provide a fast, multifaceted, and intuitive comparison. By providing a
condensed per-topic comparison visualization to the user, we are able to
display aggregate information from the entire set of reviews while providing an
interface that is at least as compact as the "most helpful reviews" currently
displayed by Amazon, yet far more informative
A Kernel Classification Framework for Metric Learning
Learning a distance metric from the given training samples plays a crucial
role in many machine learning tasks, and various models and optimization
algorithms have been proposed in the past decade. In this paper, we generalize
several state-of-the-art metric learning methods, such as large margin nearest
neighbor (LMNN) and information theoretic metric learning (ITML), into a kernel
classification framework. First, doublets and triplets are constructed from the
training samples, and a family of degree-2 polynomial kernel functions are
proposed for pairs of doublets or triplets. Then, a kernel classification
framework is established, which can not only generalize many popular metric
learning methods such as LMNN and ITML, but also suggest new metric learning
methods, which can be efficiently implemented, interestingly, by using the
standard support vector machine (SVM) solvers. Two novel metric learning
methods, namely doublet-SVM and triplet-SVM, are then developed under the
proposed framework. Experimental results show that doublet-SVM and triplet-SVM
achieve competitive classification accuracies with state-of-the-art metric
learning methods such as ITML and LMNN but with significantly less training
time.Comment: 11 pages, 7 figure
Decomposition-Based Transfer Distance Metric Learning for Image Classification
Distance metric learning (DML) is a critical factor for image analysis and
pattern recognition. To learn a robust distance metric for a target task, we
need abundant side information (i.e., the similarity/dissimilarity pairwise
constraints over the labeled data), which is usually unavailable in practice
due to the high labeling cost. This paper considers the transfer learning
setting by exploiting the large quantity of side information from certain
related, but different source tasks to help with target metric learning (with
only a little side information). The state-of-the-art metric learning
algorithms usually fail in this setting because the data distributions of the
source task and target task are often quite different. We address this problem
by assuming that the target distance metric lies in the space spanned by the
eigenvectors of the source metrics (or other randomly generated bases). The
target metric is represented as a combination of the base metrics, which are
computed using the decomposed components of the source metrics (or simply a set
of random bases); we call the proposed method, decomposition-based transfer DML
(DTDML). In particular, DTDML learns a sparse combination of the base metrics
to construct the target metric by forcing the target metric to be close to an
integration of the source metrics. The main advantage of the proposed method
compared with existing transfer metric learning approaches is that we directly
learn the base metric coefficients instead of the target metric. To this end,
far fewer variables need to be learned. We therefore obtain more reliable
solutions given the limited side information and the optimization tends to be
faster. Experiments on the popular handwritten image (digit, letter)
classification and challenge natural image annotation tasks demonstrate the
effectiveness of the proposed method
Symmetry-invariant optimization in deep networks
Recent works have highlighted scale invariance or symmetry that is present in
the weight space of a typical deep network and the adverse effect that it has
on the Euclidean gradient based stochastic gradient descent optimization. In
this work, we show that these and other commonly used deep networks, such as
those which use a max-pooling and sub-sampling layer, possess more complex
forms of symmetry arising from scaling based reparameterization of the network
weights. We then propose two symmetry-invariant gradient based weight updates
for stochastic gradient descent based learning. Our empirical evidence based on
the MNIST dataset shows that these updates improve the test performance without
sacrificing the computational efficiency of the weight updates. We also show
the results of training with one of the proposed weight updates on an image
segmentation problem.Comment: Submitted to ICLR 2016. arXiv admin note: text overlap with
arXiv:1511.0102
Improving Performance of Self-Organising Maps with Distance Metric Learning Method
Self-Organising Maps (SOM) are Artificial Neural Networks used in Pattern
Recognition tasks. Their major advantage over other architectures is human
readability of a model. However, they often gain poorer accuracy. Mostly used
metric in SOM is the Euclidean distance, which is not the best approach to some
problems. In this paper, we study an impact of the metric change on the SOM's
performance in classification problems. In order to change the metric of the
SOM we applied a distance metric learning method, so-called 'Large Margin
Nearest Neighbour'. It computes the Mahalanobis matrix, which assures small
distance between nearest neighbour points from the same class and separation of
points belonging to different classes by large margin. Results are presented on
several real data sets, containing for example recognition of written digits,
spoken letters or faces.Comment: 9 pages, 2 figure
- …