Search

Aiming at the problem of small infrared target detection, improved structural collaborative sparseness (ISCS) detection algorithm which combined sparseness and edge-preserve smoothing was put forward. Firstly, in order to improve the background, component of bigger gradient was extracted based on zero norm of gradient; Secondly, background sparseness was modeled by row norm; At last, small dim infrared target was located by column norm of error matrix. Against other detection algorithms, results of experiments show that ISCS detection algorithm can explore relationships between pieces of background, restrain clutter and enhance performance of the small dim infrared target detection algorithm against complex environment.

Recently, the so-called Support Feature Machine (SFM) was proposed as a novel approach to feature selection for classification, based on minimisation of the zero norm of a separating hyper plane. We propose an extension for linearly non-separable datasets that allows a direct trade-off between the number of misclassified data points and the number of dimensions. Results on toy examples as well as real-world datasets demonstrate that this method is able to identify relevant features very effectively.

The application of one-class machine learning is gaining attention in the computational biology community. Different studies have described the use of two-class machine learning to predict microRNAs (miRNAs) gene target. Most of these methods require the generation of an artificial negative class. However, designation of the negative class can be problematic and if it is not properly done can affect the performance of the classifier dramatically and/or yield a biased estimate of performance. We present study using one-class machine learning for miRNA target discovery and compare one-class to two-class approaches using a zero-norm for feature selection. The usage of this simple feature selection cause an improving of the one-class results, where in some cases reaches the performance of the two-class approach.

We propose the so-called Support Feature Machine (SFM) as a novel approach to feature selection for classification, based on minimisation of the zero norm of a separating hyperplane. Thus, a classifier with inherent feature selection capabilities is obtained within a single training run. Results on toy examples demonstrate that this method is able to identify relevant features very effectively.