SparseCodePicking: feature extraction in mass spectrometry using sparse coding algorithms
نویسندگان
چکیده
Mass spectrometry (MS) is an important technique for chemical profiling which calculates for a sample a high dimensional histogram-like spectrum. A crucial step of MS data processing is the peak picking which selects peaks containing information about molecules with high concentrations which are of interest in an MS investigation. We present a new procedure of the peak picking based on a sparse coding algorithm. Given a set of spectra of different classes, i.e. with different positions and heights of the peaks, this procedure can extract peaks by means of unsupervised learning. Instead of an l1-regularization penalty term used in the original sparse coding algorithm we propose using an elastic-net penalty term for better regularization. The evaluation is done by means of simulation. We show that for a large region of parameters the proposed peak picking method based on the sparse coding features outperforms a mean spectrum-based method. Moreover, we demonstrate the procedure applying it to two real-life datasets.
منابع مشابه
Face Recognition in Thermal Images based on Sparse Classifier
Despite recent advances in face recognition systems, they suffer from serious problems because of the extensive types of changes in human face (changes like light, glasses, head tilt, different emotional modes). Each one of these factors can significantly reduce the face recognition accuracy. Several methods have been proposed by researchers to overcome these problems. Nonetheless, in recent ye...
متن کاملHyperspectral Image Classification Based on the Fusion of the Features Generated by Sparse Representation Methods, Linear and Non-linear Transformations
The ability of recording the high resolution spectral signature of earth surface would be the most important feature of hyperspectral sensors. On the other hand, classification of hyperspectral imagery is known as one of the methods to extracting information from these remote sensing data sources. Despite the high potential of hyperspectral images in the information content point of view, there...
متن کاملImage Classification via Sparse Representation and Subspace Alignment
Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to d...
متن کاملLearning Feature Hierarchies for Object Recognition
In this thesis we study unsupervised learning algorithms for training feature extractors and building deep learning models. We propose sparse-modeling algorithms as the foundation for unsupervised feature extraction systems. To reduce the cost of the inference process required to obtain the optimal sparse code, we model a feed-forward function that is trained to predict this optimal sparse code...
متن کاملStructural Similarity Sparse Coding
Sparse coding theory demonstrates that the neurons in primary visual cortex form a sparse representation of natural scenes in the viewpoint of statistics. In this paper, we propose a novel sparse coding model based on structural similarity for natural image patch feature extraction. The advantage for our model is to be able to preserve structural information from a scene, which human visual per...
متن کامل