We propose a newalgorithm for classification of transcriptomic data based on the two stage procedure of feature selection. The construction of the newfeature set is based on the hypothesis that in many transcriptomic datasets there is an additional hidden structure dictated by some biological factors,...
In unsupervised learning, all the sensor data are passed to the algorithm which automatically identifies a certain number of states or data clusters, each of which may correspond to a particular activity. The most common unsupervised learning method is cluster analysis, which is used for exploratory...
Classification in data mining involves classifying a set of data instances into predefined classes. Learn more about its types and features with this blog.
Data preprocessing can improve the FDD performance of multiclass classification-based method. Han et al. introduced PCA to preprocess data for the SVM-based chiller FDD method [68]. Yan et al. introduced an auto-regressive model with exogenous variables algorithm to construct a high dimensional pa...
In addition, we validated our method with voltage-sensitive dye imaging data of Alzheimer’s disease (AD) model mice. Our analysis algorithm successfully distinguished the activity data of AD mice from that of wild type with significantly higher performance than previously suggested methods. Our ...
MSC is a commonly used algorithm for hyperspectral data preprocessing. It can effectively eliminate spectral differences due to different scattering levels, and enhance the correlation between spectra and data. MSC corrects the baseline translation and offsets phenomena of spectral data. The detailed ...
A collection of important graph embedding, classification and representation learning papers with implementations. deepwalk kernel-methods attention-mechanism network-embedding graph-kernel graph-kernels graph-convolutional-networks classification-algorithm node2vec weisfeiler-lehman graph-embedding graph-classification...
fitcsvm supports mapping the predictor data using kernel functions, and supports sequential minimal optimization (SMO), iterative single data algorithm (ISDA), or L1 soft-margin minimization via quadratic programming for objective-function minimization. To train a linear SVM model for binary ...
Its output is a classification of each contig in the input as phage, plasmid, chromosome or uncertain. Figure 1 shows the workflow of 3CAC algorithm. The details of the algorithm are described below.Fig. 1. Workflow of 3CAC algorithm. (a) Generating the initial classification using existing ...
Representation learning using a DNN requires a large set of training examples [10]. This is the main motivation for this study to use a DNN as a representation learner and a traditional ML algorithm as a classifier. A DNN is trained on the original training examples, but for the classificat...