24,475 research outputs found
Positive Semidefinite Metric Learning Using Boosting-like Algorithms
The success of many machine learning and pattern recognition methods relies
heavily upon the identification of an appropriate distance metric on the input
data. It is often beneficial to learn such a metric from the input training
data, instead of using a default one such as the Euclidean distance. In this
work, we propose a boosting-based technique, termed BoostMetric, for learning a
quadratic Mahalanobis distance metric. Learning a valid Mahalanobis distance
metric requires enforcing the constraint that the matrix parameter to the
metric remains positive definite. Semidefinite programming is often used to
enforce this constraint, but does not scale well and easy to implement.
BoostMetric is instead based on the observation that any positive semidefinite
matrix can be decomposed into a linear combination of trace-one rank-one
matrices. BoostMetric thus uses rank-one positive semidefinite matrices as weak
learners within an efficient and scalable boosting-based learning process. The
resulting methods are easy to implement, efficient, and can accommodate various
types of constraints. We extend traditional boosting algorithms in that its
weak learner is a positive semidefinite matrix with trace and rank being one
rather than a classifier or regressor. Experiments on various datasets
demonstrate that the proposed algorithms compare favorably to those
state-of-the-art methods in terms of classification accuracy and running time.Comment: 30 pages, appearing in Journal of Machine Learning Researc
Incremental Training of a Detector Using Online Sparse Eigen-decomposition
The ability to efficiently and accurately detect objects plays a very crucial
role for many computer vision tasks. Recently, offline object detectors have
shown a tremendous success. However, one major drawback of offline techniques
is that a complete set of training data has to be collected beforehand. In
addition, once learned, an offline detector can not make use of newly arriving
data. To alleviate these drawbacks, online learning has been adopted with the
following objectives: (1) the technique should be computationally and storage
efficient; (2) the updated classifier must maintain its high classification
accuracy. In this paper, we propose an effective and efficient framework for
learning an adaptive online greedy sparse linear discriminant analysis (GSLDA)
model. Unlike many existing online boosting detectors, which usually apply
exponential or logistic loss, our online algorithm makes use of LDA's learning
criterion that not only aims to maximize the class-separation criterion but
also incorporates the asymmetrical property of training data distributions. We
provide a better alternative for online boosting algorithms in the context of
training a visual object detector. We demonstrate the robustness and efficiency
of our methods on handwriting digit and face data sets. Our results confirm
that object detection tasks benefit significantly when trained in an online
manner.Comment: 14 page
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