106,354 research outputs found
Large margin metric learning for multi-label prediction
Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Canonical correlation analysis (CCA) and maximum margin output coding (MMOC) methods have shown promising results for multi-label prediction, where each instance is associated with multiple labels. However, these methods require an expensive decoding procedure to recover the multiple labels of each testing instance. The testing complexity becomes unacceptable when there are many labels. To avoid decoding completely, we present a novel large margin metric learning paradigm for multi-label prediction. In particular, the proposed method learns a distance metric to discover label dependency such that instances with very different multiple labels will be moved far away. To handle many labels, we present an accelerated proximal gradient procedure to speed up the learning process. Comprehensive experiments demonstrate that our proposed method is significantly faster than CCA and MMOC in terms of both training and testing complexities. Moreover, our method achieves superior prediction performance compared with state-of-the-art methods
RandomBoost: Simplified Multi-class Boosting through Randomization
We propose a novel boosting approach to multi-class classification problems,
in which multiple classes are distinguished by a set of random projection
matrices in essence. The approach uses random projections to alleviate the
proliferation of binary classifiers typically required to perform multi-class
classification. The result is a multi-class classifier with a single
vector-valued parameter, irrespective of the number of classes involved. Two
variants of this approach are proposed. The first method randomly projects the
original data into new spaces, while the second method randomly projects the
outputs of learned weak classifiers. These methods are not only conceptually
simple but also effective and easy to implement. A series of experiments on
synthetic, machine learning and visual recognition data sets demonstrate that
our proposed methods compare favorably to existing multi-class boosting
algorithms in terms of both the convergence rate and classification accuracy.Comment: 15 page
Evaluation of Joint Multi-Instance Multi-Label Learning For Breast Cancer Diagnosis
Multi-instance multi-label (MIML) learning is a challenging problem in many
aspects. Such learning approaches might be useful for many medical diagnosis
applications including breast cancer detection and classification. In this
study subset of digiPATH dataset (whole slide digital breast cancer
histopathology images) are used for training and evaluation of six
state-of-the-art MIML methods.
At the end, performance comparison of these approaches are given by means of
effective evaluation metrics. It is shown that MIML-kNN achieve the best
performance that is %65.3 average precision, where most of other methods attain
acceptable results as well
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