Clustering in high-dimensional spaces is nowadays a recurrent problem in many scientific domains but remains a difficult problem. This is mainly due to the fact that high-dimensional data usually live in low-dimensional subspaces hidden in the original space. This paper presents a model-based clustering approach which models the data in a discriminative subspace with an intrinsic dimension lowe...

Neural machine translation (NMT) heavily relies on an attention network to produce a context vector for each target word prediction. In practice, we find that context vectors for different target words are quite similar to one another and therefore are insufficient in discriminatively predicting target words. The reason for this might be that context vectors produced by the vanilla attention ne...

Learning domain-invariant features is of vital importance to unsupervised domain adaptation, where classifiers trained on the source domain need to be adapted to a different target domain for which no labeled examples are available. In this paper, we propose a novel approach for learning such features. The central idea is to exploit the existence of landmarks, which are a subset of labeled data...

A popular approach for both classification and clustering is to assume that data is generated by a mixture model. Support of the mixture model for classification has waned as discriminative models have gained popularity. However, the mixture model is a valuable element of statistics and remains useful for clustering. Furthermore, reasoning about mixture models is often relatively simple, so the...

Logistic regression is presumably the most popular representative of probabilistic discriminative classifiers. In this paper, a kernel variant of logistic regression is introduced as an iteratively re-weighted least-squares algorithm in kernel-induced feature spaces. This formulation allows us to apply highly efficient approximation methods that are capable of dealing with large-scale problems....

cry genes encoding cry proteins toxic to lepidoptera, coleoptera and diptera species were studied in thirty seven b. thuringiensis strains isolated from twelve naturally infested heliothis armigera larvae. to further confirm the isolates, two groups of species-indicative biochemical tests were applied while discriminative biochemical tests being employed to figure out the repetitive strains. a ...

Local Binary Patterns (LBP) have been well exploited for facial image analysis recently. In the existing work, the LBP histograms are extracted from local facial regions, and used as a whole for the regional description. However, not all bins in the LBP histogram are necessary to be useful for facial representation. In this paper, we propose to learn discriminative LBP-Histogram (LBPH) bins for...

The performance of a speech recogniser, or of any other pattern classifier, strongly depends on the input features: to obtain a good performance, the feature set needs to be both highly discriminative and compact. Linear discriminant analysis (LDA) is a common data-driven method used to find linear transformations that map large feature vectors onto smaller ones while retaining most of the disc...

Recent investigations on the stability of proteins have demonstrated various structural factors, but few have considered sequence factors such as protein motifs. These motifs represent highly conserved regions and describe critical regions that may only exist on proteins that remain functional at high temperatures. This investigation presents a method for identifying and comparing corresponding...

Discriminative dictionary learning (DL) has been widely studied in various pattern classification problems. Most of the existing DL methods aim to learn a synthesis dictionary to represent the input signal while enforcing the representation coefficients and/or representation residual to be discriminative. However, the `0 or `1-norm sparsity constraint on the representation coefficients adopted ...