common spatial pattern (csp) is a method commonly used to enhance the effects of event‑related desynchronization and event‑related synchronization present in multichannel electroencephalogram‑based brain‑computer interface (bci) systems. in the present study, a novel csp sub‑band feature selection has been proposed based on the discriminative information of the features. besides, a distinction ...

Two recent methods, Neighborhood Components Analysis (NCA) and Informative Discriminant Analysis (IDA), search for a class-discriminative subspace or discriminative components of data, equivalent to learning of distance metrics invariant to changes perpendicular to the subspace. Constraining metrics to a subspace is useful for regularizing the metrics, and for dimensionality reduction. We intro...

Police agencies have been collecting increasing amount of information to better understand patterns in criminal activity. Recently there is a new trend on using the data collected to predict where and when crime will occur. Crime prediction is greatly beneficial because if it is done accurately, police practitioner would be able to allocate resources to the geographic areas most at risk for cri...

Linear Discriminative Analysis techniques have been used in pattern recognition to map feature vectors to achieve optimal classification. Kernel Discriminative Analysis(KDA) seeks to introduce non-linearity in this approach by mapping the features to a non-linear space before applying LDA analysis. The formulation is expressed as an eigenvalue problem resolution. Using a different kernel, one c...

The "discriminative direction" has been proven useful to reveal the subtle difference between two anatomical shape classes. When a shape moves along this direction, its deformation will best manifest the class difference detected by a kernel classifier. However, we observe that such a direction cannot maintain a shape's "anatomical" correctness, introducing spurious difference. To overcome this...

Feature selection has been proven to be efficient in preparing high dimensional data for data mining and machine learning. As most data is unlabeled, unsupervised feature selection has attracted more and more attention in recent years. Discriminant analysis has been proven to be a powerful technique to select discriminative features for supervised feature selection. To apply discriminant analys...

This work presents a new and efficient approach to discriminative speaker verification in the i–vector space. We illustrate the development of a linear discriminative classifier that is trained to discriminate between the hypothesis that a pair of feature vectors in a trial belong to the same speaker or to different speakers. This approach is alternative to the usual discriminative setup that d...

Feature is important for many applications in biomedical signal analysis and living system analysis. A fast discriminative stochastic neighbor embedding analysis (FDSNE) method for feature extraction is proposed in this paper by improving the existing DSNE method. The proposed algorithm adopts an alternative probability distribution model constructed based on its K-nearest neighbors from the in...

Optimal Component Analysis (OCA) is a linear subspace technique for dimensionality reduction designed to optimize object classification and recognition performance. The linear nature of OCA often limits recognition performance, if the underlying data structure is nonlinear or cluster structures are complex. To address these problems, we investigate a kernel analogue of OCA, which consists of ap...

MOTIVATION High-throughput ChIP-seq studies typically identify thousands of peaks for a single transcription factor (TF). It is common for traditional motif discovery tools to predict motifs that are statistically significant against a naïve background distribution but are of questionable biological relevance. RESULTS We describe a simple yet effective algorithm for discovering differential m...