In recent years, unsupervised feature selection methods have raised considerable interest in
many research areas; this is mainly due to their ability to identify and select relevant features …

The massive growth of data in recent years has led challenges in data mining and machine
learning tasks. One of the major challenges is the selection of relevant features from the …

The tool wear monitoring (TWM) system which can estimate the tool wear state and predict
remaining useful life (RUL) of the tool plays an important role in micro-milling because of the …

We consider the problem of selecting the “best” subset of exactly k columns from an m× n
matrix A. In particular, we present and analyze a novel two-stage algorithm that runs in O …

The potential of selected spectroscopic methods-UV–Vis, synchronous fluorescence and
NIR as well a data fusion of the measurements by these methods-for the classification of tea …

A new unsupervised forward orthogonal search (FOS) algorithm is introduced for feature
selection and ranking. In the new algorithm, features are selected in a stepwise way, one at …

We present a novel feature selection algorithm for the $ k $-means clustering problem. Our
algorithm is randomized and, assuming an accuracy parameter $\epsilon\in (0, 1) $, selects …

In countries where fish is often consumed, fish bones are some of the most frequently
ingested foreign bodies encountered in foods. In the production of fish fillets, fish bone …

The construction of tunnels has serious geomechanical uncertainties involving matters of
both safety and budget. Nowadays, modern machinery gathers very useful information about …

Principal Components Analysis (PCA) is the predominant linear dimensionality reduction
technique, and has been widely applied on datasets in all scientific domains. We consider …