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 a...

In this paper, we propose to adapt the batch version of selforganizing map (SOM) to background information in clustering task. It deals with constrained clustering with SOM in a deterministic paradigm. In this context we adapt the appropriate topological clustering to pairwise instance level constraints with the study of their informativeness and coherence properties for measuring their utility...

We cluster verbs into lexical semantic classes, using a general set of noisy features that capture syntactic and semantic properties of the verbs. The feature set was previously shown to work well in a supervised learning setting, using known English verb classes. In moving to a scenario of verb class discovery, using clustering, we face the problem of having a large number of irrelevant featur...

In semi-supervised learning, a number of labeled examples are usually required for training an initial weakly useful predictor which is in turn used for exploiting the unlabeled examples. However, in many real-world applications there may exist very few labeled training examples, which makes the weakly useful predictor difficult to generate, and therefore these semisupervised learning methods c...

Graph theory has been shown to provide a powerful tool for representing and tackling machine learning problems, such as clustering, semi-supervised learning, and feature ranking. This paper proposes a graph-based discrete differential operator for detecting and eliminating competence-critical instances and class label noise from a training set in order to improve classification performance. Res...

When studying a metastable dynamical system, a prime concern is how to decompose the phase space into a set of metastable states. Unfortunately, the metastable state decomposition based on simulation or experimental data is still a challenge. The most popular and simplest approach is geometric clustering which is developed based on the classical clustering technique. However, the prerequisites ...

Sentiment classification is a very domainspecific problem; classifiers trained in one domain do not perform well in others. Unfortunately, many domains are lacking in large amounts of labeled data for fully-supervised learning approaches. At the same time, sentiment classifiers need to be customizable to new domains in order to be useful in practice. We attempt to address these difficulties and...

Heteroscedastic Linear Discriminant Analysis (HLDA) was introduced in [1] as an extension of Linear Discriminant Analysis to the case where the class-conditional distributions have unequal covariances. The HLDA transform is computed such that the likelihood of the training (labeled) data is maximized, under the constraint that the projected distributions are orthogonal to a nuisance space that ...

This paper presents a semi-supervised kernel-based fuzzy c-means algorithm called S2KFCM by introducing semi-supervised learning technique and the kernel method simultaneously into conventional fuzzy clustering algorithm. Through using labeled and unlabeled data together, S2KFCM can be applied to both clustering and classification tasks. However, only the latter is concerned in this paper. Expe...

In this paper we describe the Spanish Lexical Sample task. This task was initially devised for evaluating the role of unlabeled examples in supervised and semi-supervised learning systems for WSD and it was coordinated with five other lexical sample tasks (Basque, Catalan, English, Italian, and Rumanian) in order to share part of the target words. Firstly, we describe the methodology followed t...