Results 111 to 120 of about 282,422 (272)
Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning
Advanced Intelligent Discovery, EarlyView.Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang +4 more
wiley +1 more source
Sparse Linear Discriminant Analysis With Constant Between-Class Distance for Feature Selection
IEEE AccessFeature selection is an important preprocessing step in machine learning to remove irrelevant and redundant features. Due to its ability to effectively maintain the discriminability of extracted features, Trace Ratio Linear Discriminant Analysis (TR-LDA)
Shuangle Guo +5 more
doaj +1 more source
Advanced microwave soil moisture studies [PDF]
Comparisons of low level L-band brightness temperature (TB) and thermal infrared (TIR) data as well as the following data sets: soil map and land cover data; direct soil moisture measurement; and a computer generated contour map were statistically ...
Dalsted, K. J., Harlan, J. C.
core +1 more source
Chemical VOC sensing mechanism of sol-gel ZnO pellets and linear discriminant analysis for instantaneous selectivity. [PDF]
RSC Adv, 2023 Souissi R +5 more
europepmc +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.Quantitative phase maps of single cells recorded in flow cytometry modality feed a hierarchical architecture of machine learning models for the label‐free identification of subtypes of ovarian cancer. The employment of a priori clinical information improves the classification performance, thus emulating the clinical application of liquid biopsy during ...
Daniele Pirone +11 more
wiley +1 more source
Robust linear discriminant analysis for multiple groups: influence and classification efficiencies. [PDF]
Linear discriminant analysis for multiple groups is typically carried out using Fisher's method. This method relies on the sample averages and covariance ma- trices computed from the different groups constituting the training sample.
Croux, Christophe +2 more
core
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.The use of image quality metrics in combination with machine learning enables automatic image quality assessment for fluorescence microscopy images. The method can be integrated into the experimental pipeline for optical microscopy and utilized to classify artifacts in experimental images and to build quality rankings with a reference‐free approach ...
Elena Corbetta, Thomas Bocklitz
wiley +1 more source
A Convex Formulation for Linear Discriminant Analysis
We present a supervised dimensionality reduction technique called Convex Linear Discriminant Analysis (ConvexLDA). The proposed model optimizes a multi-objective cost function by balancing two complementary terms. The first term pulls the samples of a class towards its centroid by minimizing a sample's distance from its class-centroid in low ...
Sai Vijay Kumar Surineela +3 more
openaire +2 more sources
A linear discriminant analysis model of imbalanced associative learning in the mushroom body compartment. [PDF]
PLoS Comput Biol, 2023 Lipshutz D +3 more
europepmc +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.A skin‐conformal wearable device based on laser‐induced graphene is developed for continuous strain measurement across the circumference of the forearm for gesture recognition and hand‐tracking applications. Post material optimization, the strain sensor array is integrated with a wearable wireless readout circuit for real‐time control of a robotic arm,
Vinay Kammarchedu +2 more
wiley +1 more source