2,728 research outputs found
Out-of-sample generalizations for supervised manifold learning for classification
Supervised manifold learning methods for data classification map data samples
residing in a high-dimensional ambient space to a lower-dimensional domain in a
structure-preserving way, while enhancing the separation between different
classes in the learned embedding. Most nonlinear supervised manifold learning
methods compute the embedding of the manifolds only at the initially available
training points, while the generalization of the embedding to novel points,
known as the out-of-sample extension problem in manifold learning, becomes
especially important in classification applications. In this work, we propose a
semi-supervised method for building an interpolation function that provides an
out-of-sample extension for general supervised manifold learning algorithms
studied in the context of classification. The proposed algorithm computes a
radial basis function (RBF) interpolator that minimizes an objective function
consisting of the total embedding error of unlabeled test samples, defined as
their distance to the embeddings of the manifolds of their own class, as well
as a regularization term that controls the smoothness of the interpolation
function in a direction-dependent way. The class labels of test data and the
interpolation function parameters are estimated jointly with a progressive
procedure. Experimental results on face and object images demonstrate the
potential of the proposed out-of-sample extension algorithm for the
classification of manifold-modeled data sets
Reducing the Effects of Detrimental Instances
Not all instances in a data set are equally beneficial for inducing a model
of the data. Some instances (such as outliers or noise) can be detrimental.
However, at least initially, the instances in a data set are generally
considered equally in machine learning algorithms. Many current approaches for
handling noisy and detrimental instances make a binary decision about whether
an instance is detrimental or not. In this paper, we 1) extend this paradigm by
weighting the instances on a continuous scale and 2) present a methodology for
measuring how detrimental an instance may be for inducing a model of the data.
We call our method of identifying and weighting detrimental instances reduced
detrimental instance learning (RDIL). We examine RIDL on a set of 54 data sets
and 5 learning algorithms and compare RIDL with other weighting and filtering
approaches. RDIL is especially useful for learning algorithms where every
instance can affect the classification boundary and the training instances are
considered individually, such as multilayer perceptrons trained with
backpropagation (MLPs). Our results also suggest that a more accurate estimate
of which instances are detrimental can have a significant positive impact for
handling them.Comment: 6 pages, 5 tables, 2 figures. arXiv admin note: substantial text
overlap with arXiv:1403.189
Robust supervised classification with mixture models: Learning from data with uncertain labels
International audienceIn the supervised classification framework, human supervision is required for labeling a set of learning data which are then used for building the classifier. However, in many applications, human supervision is either imprecise, difficult or expensive. In this paper, the problem of learning a supervised multi-class classifier from data with uncertain labels is considered and a model-based classification method is proposed to solve it. The idea of the proposed method is to confront an unsupervised modelling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels. Experiments on artificial and real data are provided to highlight the main features of the proposed method as well as an application to object recognition under weak supervision
Robust Loss Functions under Label Noise for Deep Neural Networks
In many applications of classifier learning, training data suffers from label
noise. Deep networks are learned using huge training data where the problem of
noisy labels is particularly relevant. The current techniques proposed for
learning deep networks under label noise focus on modifying the network
architecture and on algorithms for estimating true labels from noisy labels. An
alternate approach would be to look for loss functions that are inherently
noise-tolerant. For binary classification there exist theoretical results on
loss functions that are robust to label noise. In this paper, we provide some
sufficient conditions on a loss function so that risk minimization under that
loss function would be inherently tolerant to label noise for multiclass
classification problems. These results generalize the existing results on
noise-tolerant loss functions for binary classification. We study some of the
widely used loss functions in deep networks and show that the loss function
based on mean absolute value of error is inherently robust to label noise. Thus
standard back propagation is enough to learn the true classifier even under
label noise. Through experiments, we illustrate the robustness of risk
minimization with such loss functions for learning neural networks.Comment: Appeared in AAAI 201
Probabilistic Fisher discriminant analysis: A robust and flexible alternative to Fisher discriminant analysis
International audienceFisher discriminant analysis (FDA) is a popular and powerful method for dimensionality reduction and classification. Unfortunately, the optimality of the dimension reduction provided by FDA is only proved in the homoscedastic case. In addition, FDA is known to have poor performances in the cases of label noise and sparse labeled data. To overcome these limitations, this work proposes a probabilistic framework for FDA which relaxes the homoscedastic assumption on the class covariance matrices and adds a term to explicitly model the non-discriminative information. This allows the proposed method to be robust to label noise and to be used in the semi-supervised context. Experiments on real-world datasets show that the proposed approach works at least as well as FDA in standard situations and outperforms it in the label noise and sparse label cases
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