10,008 research outputs found
A random matrix analysis and improvement of semi-supervised learning for large dimensional data
This article provides an original understanding of the behavior of a class of
graph-oriented semi-supervised learning algorithms in the limit of large and
numerous data. It is demonstrated that the intuition at the root of these
methods collapses in this limit and that, as a result, most of them become
inconsistent. Corrective measures and a new data-driven parametrization scheme
are proposed along with a theoretical analysis of the asymptotic performances
of the resulting approach. A surprisingly close behavior between theoretical
performances on Gaussian mixture models and on real datasets is also
illustrated throughout the article, thereby suggesting the importance of the
proposed analysis for dealing with practical data. As a result, significant
performance gains are observed on practical data classification using the
proposed parametrization
Semi-supervised transductive speaker identification
We present an application of transductive semi-supervised learning to the problem of speaker identification. Formulating this problem as one of transduction is the most natural choice in some scenarios, such as when annotating archived speech data. Experiments with the CHAINS corpus show that, using the basic MFCC-encoding of recorded utterances, a well known simple semi-supervised algorithm, label spread, can solve this problem well. With only a small number of labelled utterances, the semi-supervised algorithm drastically outperforms a state of the art supervised support vector machine algorithm. Although we restrict ourselves to the transductive setting in this paper, the results encourage future work on semi-supervised learning for inductive speaker identification
Minimum Density Hyperplanes
Associating distinct groups of objects (clusters) with contiguous regions of
high probability density (high-density clusters), is central to many
statistical and machine learning approaches to the classification of unlabelled
data. We propose a novel hyperplane classifier for clustering and
semi-supervised classification which is motivated by this objective. The
proposed minimum density hyperplane minimises the integral of the empirical
probability density function along it, thereby avoiding intersection with high
density clusters. We show that the minimum density and the maximum margin
hyperplanes are asymptotically equivalent, thus linking this approach to
maximum margin clustering and semi-supervised support vector classifiers. We
propose a projection pursuit formulation of the associated optimisation problem
which allows us to find minimum density hyperplanes efficiently in practice,
and evaluate its performance on a range of benchmark datasets. The proposed
approach is found to be very competitive with state of the art methods for
clustering and semi-supervised classification
Semi-Supervised Sound Source Localization Based on Manifold Regularization
Conventional speaker localization algorithms, based merely on the received
microphone signals, are often sensitive to adverse conditions, such as: high
reverberation or low signal to noise ratio (SNR). In some scenarios, e.g. in
meeting rooms or cars, it can be assumed that the source position is confined
to a predefined area, and the acoustic parameters of the environment are
approximately fixed. Such scenarios give rise to the assumption that the
acoustic samples from the region of interest have a distinct geometrical
structure. In this paper, we show that the high dimensional acoustic samples
indeed lie on a low dimensional manifold and can be embedded into a low
dimensional space. Motivated by this result, we propose a semi-supervised
source localization algorithm which recovers the inverse mapping between the
acoustic samples and their corresponding locations. The idea is to use an
optimization framework based on manifold regularization, that involves
smoothness constraints of possible solutions with respect to the manifold. The
proposed algorithm, termed Manifold Regularization for Localization (MRL), is
implemented in an adaptive manner. The initialization is conducted with only
few labelled samples attached with their respective source locations, and then
the system is gradually adapted as new unlabelled samples (with unknown source
locations) are received. Experimental results show superior localization
performance when compared with a recently presented algorithm based on a
manifold learning approach and with the generalized cross-correlation (GCC)
algorithm as a baseline
Evaluation of Hashing Methods Performance on Binary Feature Descriptors
In this paper we evaluate performance of data-dependent hashing methods on
binary data. The goal is to find a hashing method that can effectively produce
lower dimensional binary representation of 512-bit FREAK descriptors. A
representative sample of recent unsupervised, semi-supervised and supervised
hashing methods was experimentally evaluated on large datasets of labelled
binary FREAK feature descriptors
Semi-supervised prediction of protein interaction sentences exploiting semantically encoded metrics
Protein-protein interaction (PPI) identification is an integral component of many biomedical research and database curation tools. Automation of this task through classification is one of the key goals of text mining (TM). However, labelled PPI corpora required to train classifiers are generally small. In order to overcome this sparsity in the training data, we propose a novel method of integrating corpora that do not contain relevance judgements. Our approach uses a semantic language model to gather word similarity from a large unlabelled corpus. This additional information is integrated into the sentence classification process using kernel transformations and has a re-weighting effect on the training features that leads to an 8% improvement in F-score over the baseline results. Furthermore, we discover that some words which are generally considered indicative of interactions are actually neutralised by this process
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