3 citations · 15 across the 12 of their papers we have counts for
8 papers · 1 filter
TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models
Guy Zamberg, Moshe Salhov, Ofir Lindenbaum +1
Tables are an abundant form of data with use cases across all scientific fields. Real-world datasets often contain anomalous samples that can negatively affect downstream analysis.…
Imbalanced Classification via a Tabular Translation GAN
Jonathan Gradstein, Moshe Salhov, Yoav Tulpan +2
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…
-based Sparse Canonical Correlation Analysis
Ofir Lindenbaum, Moshe Salhov, Amir Averbuch +1
Canonical Correlation Analysis (CCA) models are powerful for studying the associations between two sets of variables. The canonically correlated representations, termed \textit{can…
Majority Voting and the Condorcet's Jury Theorem
Hanan Shteingart, Eran Marom, Igor Itkin +4
There is a striking relationship between a three hundred years old Political Science theorem named "Condorcet's jury theorem" (1785), which states that majorities are more likely t…
Kernel Scaling for Manifold Learning and Classification
Ofir Lindenbaum, Moshe Salhov, Arie Yeredor +1
Kernel methods play a critical role in many machine learning algorithms. They are useful in manifold learning, classification, clustering and other data analysis tasks. Setting the…
Incomplete Pivoted QR-based Dimensionality Reduction
Amit Bermanis, Aviv Rotbart, Moshe Salhov +1
High-dimensional big data appears in many research fields such as image recognition, biology and collaborative filtering. Often, the exploration of such data by classic algorithms…
Multi-View Kernel Consensus For Data Analysis
Moshe Salhov, Ofir Lindenbaum, Yariv Aizenbud +3
The input data features set for many data driven tasks is high-dimensional while the intrinsic dimension of the data is low. Data analysis methods aim to uncover the underlying low…
MultiView Diffusion Maps
Ofir Lindenbaum, Arie Yeredor, Moshe Salhov +1
In this paper, we address the challenging task of achieving multi-view dimensionality reduction. The goal is to effectively use the availability of multiple views for extracting a…