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Found 11 papers, 0 papers with code
Knockoff-Inspired Feature Selection via Generative Models
While variable selection in statistics aims to distinguish between true and false predictors, feature selection in machine learning aims to reduce the dimensionality of the data while preserving the performance of the learning method.
Explainable Machine Learning for Scientific Insights and Discoveries
Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data.
Few-Shot Learning-Based Human Activity Recognition
The proposed few-shot human activity recognition method leverages a deep learning model for feature extraction and classification while knowledge transfer is performed in the manner of model parameter transfer.
Autoencoder Based Sample Selection for Self-Taught Learning
In this paper, we propose a metric for the relevance between a source sample and the target samples.
Graph Autoencoder-Based Unsupervised Feature Selection with Broad and Local Data Structure Preservation
Feature selection is a dimensionality reduction technique that selects a subset of representative features from high dimensional data by eliminating irrelevant and redundant features.
Semi-Supervised Endmember Identification In Nonlinear Spectral Mixtures Via Semantic Representation
This paper proposes a new hyperspectral unmixing method for nonlinearly mixed hyperspectral data using a semantic representation in a semi-supervised fashion, assuming the availability of a spectral reference library.
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series
This paper proposes an out-of-sample extension framework for a global manifold learning algorithm (Isomap) that uses temporal information in out-of-sample points in order to make the embedding more robust to noise and artifacts.
Masking Strategies for Image Manifolds
We consider the problem of selecting an optimal mask for an image manifold, i. e., choosing a subset of the pixels of the image that preserves the manifold's geometric structure present in the original data.
Wavelet-Based Semantic Features for Hyperspectral Signature Discrimination
Hyperspectral signature classification is a quantitative analysis approach for hyperspectral imagery which performs detection and classification of the constituent materials at the pixel level in the scene.
Perfect Recovery Conditions For Non-Negative Sparse Modeling
Sparse modeling has been widely and successfully used in many applications such as computer vision, machine learning, and pattern recognition.
Sparse Signal Recovery Using Markov Random Fields
Compressive Sensing (CS) combines sampling and compression into a single sub-Nyquist linear measurement process for sparse and compressible signals.
