1,646 research outputs found
Non-sparse Linear Representations for Visual Tracking with Online Reservoir Metric Learning
Most sparse linear representation-based trackers need to solve a
computationally expensive L1-regularized optimization problem. To address this
problem, we propose a visual tracker based on non-sparse linear
representations, which admit an efficient closed-form solution without
sacrificing accuracy. Moreover, in order to capture the correlation information
between different feature dimensions, we learn a Mahalanobis distance metric in
an online fashion and incorporate the learned metric into the optimization
problem for obtaining the linear representation. We show that online metric
learning using proximity comparison significantly improves the robustness of
the tracking, especially on those sequences exhibiting drastic appearance
changes. Furthermore, in order to prevent the unbounded growth in the number of
training samples for the metric learning, we design a time-weighted reservoir
sampling method to maintain and update limited-sized foreground and background
sample buffers for balancing sample diversity and adaptability. Experimental
results on challenging videos demonstrate the effectiveness and robustness of
the proposed tracker.Comment: Appearing in IEEE Conf. Computer Vision and Pattern Recognition, 201
Learning Heterogeneous Similarity Measures for Hybrid-Recommendations in Meta-Mining
The notion of meta-mining has appeared recently and extends the traditional
meta-learning in two ways. First it does not learn meta-models that provide
support only for the learning algorithm selection task but ones that support
the whole data-mining process. In addition it abandons the so called black-box
approach to algorithm description followed in meta-learning. Now in addition to
the datasets, algorithms also have descriptors, workflows as well. For the
latter two these descriptions are semantic, describing properties of the
algorithms. With the availability of descriptors both for datasets and data
mining workflows the traditional modelling techniques followed in
meta-learning, typically based on classification and regression algorithms, are
no longer appropriate. Instead we are faced with a problem the nature of which
is much more similar to the problems that appear in recommendation systems. The
most important meta-mining requirements are that suggestions should use only
datasets and workflows descriptors and the cold-start problem, e.g. providing
workflow suggestions for new datasets.
In this paper we take a different view on the meta-mining modelling problem
and treat it as a recommender problem. In order to account for the meta-mining
specificities we derive a novel metric-based-learning recommender approach. Our
method learns two homogeneous metrics, one in the dataset and one in the
workflow space, and a heterogeneous one in the dataset-workflow space. All
learned metrics reflect similarities established from the dataset-workflow
preference matrix. We demonstrate our method on meta-mining over biological
(microarray datasets) problems. The application of our method is not limited to
the meta-mining problem, its formulations is general enough so that it can be
applied on problems with similar requirements
Online Metric-Weighted Linear Representations for Robust Visual Tracking
In this paper, we propose a visual tracker based on a metric-weighted linear
representation of appearance. In order to capture the interdependence of
different feature dimensions, we develop two online distance metric learning
methods using proximity comparison information and structured output learning.
The learned metric is then incorporated into a linear representation of
appearance.
We show that online distance metric learning significantly improves the
robustness of the tracker, especially on those sequences exhibiting drastic
appearance changes. In order to bound growth in the number of training samples,
we design a time-weighted reservoir sampling method.
Moreover, we enable our tracker to automatically perform object
identification during the process of object tracking, by introducing a
collection of static template samples belonging to several object classes of
interest. Object identification results for an entire video sequence are
achieved by systematically combining the tracking information and visual
recognition at each frame. Experimental results on challenging video sequences
demonstrate the effectiveness of the method for both inter-frame tracking and
object identification.Comment: 51 pages. Appearing in IEEE Transactions on Pattern Analysis and
Machine Intelligenc
Structured learning of metric ensembles with application to person re-identification
Matching individuals across non-overlapping camera networks, known as person
re-identification, is a fundamentally challenging problem due to the large
visual appearance changes caused by variations of viewpoints, lighting, and
occlusion. Approaches in literature can be categoried into two streams: The
first stream is to develop reliable features against realistic conditions by
combining several visual features in a pre-defined way; the second stream is to
learn a metric from training data to ensure strong inter-class differences and
intra-class similarities. However, seeking an optimal combination of visual
features which is generic yet adaptive to different benchmarks is a unsoved
problem, and metric learning models easily get over-fitted due to the scarcity
of training data in person re-identification. In this paper, we propose two
effective structured learning based approaches which explore the adaptive
effects of visual features in recognizing persons in different benchmark data
sets. Our framework is built on the basis of multiple low-level visual features
with an optimal ensemble of their metrics. We formulate two optimization
algorithms, CMCtriplet and CMCstruct, which directly optimize evaluation
measures commonly used in person re-identification, also known as the
Cumulative Matching Characteristic (CMC) curve.Comment: 16 pages. Extended version of "Learning to Rank in Person
Re-Identification With Metric Ensembles", at
http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Paisitkriangkrai_Learning_to_Rank_2015_CVPR_paper.html.
arXiv admin note: text overlap with arXiv:1503.0154
Adaptive Algorithms For Classification On High-Frequency Data Streams: Application To Finance
Mención Internacional en el título de doctorIn recent years, the problem of concept drift has gained importance in the financial
domain. The succession of manias, panics and crashes have stressed the nonstationary
nature and the likelihood of drastic structural changes in financial markets.
The most recent literature suggests the use of conventional machine learning and statistical
approaches for this. However, these techniques are unable or slow to adapt
to non-stationarities and may require re-training over time, which is computationally
expensive and brings financial risks.
This thesis proposes a set of adaptive algorithms to deal with high-frequency data
streams and applies these to the financial domain. We present approaches to handle
different types of concept drifts and perform predictions using up-to-date models.
These mechanisms are designed to provide fast reaction times and are thus applicable
to high-frequency data. The core experiments of this thesis are based on the prediction
of the price movement direction at different intraday resolutions in the SPDR S&P 500
exchange-traded fund. The proposed algorithms are benchmarked against other popular
methods from the data stream mining literature and achieve competitive results.
We believe that this thesis opens good research prospects for financial forecasting
during market instability and structural breaks. Results have shown that our proposed
methods can improve prediction accuracy in many of these scenarios. Indeed, the
results obtained are compatible with ideas against the efficient market hypothesis.
However, we cannot claim that we can beat consistently buy and hold; therefore, we
cannot reject it.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Gustavo Recio Isasi.- Secretario: Pedro Isasi Viñuela.- Vocal: Sandra García Rodrígue
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