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Found 23 papers, 1 papers with code
Missing Entries Matrix Approximation and Completion
The approximation constraint can be any whose approximated solution is known for the full matrix.
Video Segmentation via Diffusion Bases
A common approach performs background subtraction, which identifies moving objects as the portion of a video frame that differs significantly from a background model.
Adaptive Compressed Tomography Sensing
One of the main challenges in Computed Tomography (CT) is how to balance between the amount of radiation the patient is exposed to during scan time and the quality of the CT image.
Randomized LU decomposition: An Algorithm for Dictionaries Construction
Such approach is harder to deceive and we show that only a few file fragments from a whole file are needed for a successful classification.
MultiView Diffusion Maps
The multi-view dimensionality reduction is achieved by defining a cross-view model in which an implied random walk process is restrained to hop between objects in the different views.
PCA-Based Out-of-Sample Extension for Dimensionality Reduction
We prove that the error of the proposed algorithm is bounded.
Diffusion Representations
In this paper, we present a representation framework for data analysis of datasets that is based on a closed-form decomposition of the measure-based kernel.
Gaussian Process Regression for Out-of-Sample Extension
Many of the existing methods produce a low dimensional representation that attempts to describe the intrinsic geometric structure of the original data.
Multi-View Kernel Consensus For Data Analysis
Data analysis methods aim to uncover the underlying low dimensional structure imposed by the low dimensional hidden parameters by utilizing distance metrics that consider the set of attributes as a single monolithic set.
Incomplete Pivoted QR-based Dimensionality Reduction
The dictionary enables to have a natural extension of the low-dimensional embedding to out-of-sample data points, which gives rise to a distortion-based criterion for anomaly detection.
Multi-View Kernels for Low-Dimensional Modeling of Seismic Events
The problem of learning from seismic recordings has been studied for years.
Kernel Scaling for Manifold Learning and Classification
We propose to set a scale parameter that is tailored to one of two types of tasks: classification and manifold learning.
Similarity Search Over Graphs Using Localized Spectral Analysis
This paper provides a new similarity detection algorithm.
Image inpainting using directional wavelet packets originating from polynomial splines
Tensor products of 1D qWPs provide a diversity of 2D qWPs oriented in multiple directions.
Coupling BM3D with directional wavelet packets for image denoising
The combined method consists of several iterations of qWPdn and BM3D algorithms, where the output from one algorithm updates the input to the other (cross-boosting). The qWPdn and BM3D methods complement each other.
Deep Gated Canonical Correlation Analysis
The proposed procedure learns two non-linear transformations and simultaneously gates the input variables to identify a subset of most correlated variables.
$\ell_0$-based Sparse Canonical Correlation Analysis
We further propose $\ell_0$-Deep CCA for solving the problem of non-linear sparse CCA by modeling the correlated representations using deep nets.
Automated identification of transiting exoplanet candidates in NASA Transiting Exoplanets Survey Satellite (TESS) data with machine learning methods
A novel artificial intelligence (AI) technique that uses machine learning (ML) methodologies combines several algorithms, which were developed by ThetaRay, Inc., is applied to NASA's Transiting Exoplanets Survey Satellite (TESS) dataset to identify exoplanetary candidates.
L0-Sparse Canonical Correlation Analysis
We further propose $\ell_0$-Deep CCA for solving the problem of non-linear sparse CCA by modeling the correlated representations using deep nets.
Imbalanced Classification via Explicit Gradient Learning From Augmented Data
Learning from imbalanced data is one of the most significant challenges in real-world classification tasks.
Imbalanced Classification via a Tabular Translation GAN
When presented with a binary classification problem where the data exhibits severe class imbalance, most standard predictive methods may fail to accurately model the minority class.
Cross-boosting of WNNM Image Denoising method by Directional Wavelet Packets
The paper presents an image denoising scheme by combining a method that is based on directional quasi-analytic wavelet packets (qWPs) with the state-of-the-art Weighted Nuclear Norm Minimization (WNNM) denoising algorithm.
TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models
Tables are an abundant form of data with use cases across all scientific fields.
