Search Results for author:
Found 16 papers, 2 papers with code
$\ell_0$-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.
Search and Score-based Waterfall Auction Optimization
Our framework guarantees that the waterfall revenue improves between iterations ultimately converging into a local optimum.
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.
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.
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 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.
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.
Majority Voting and the Condorcet's Jury Theorem
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 to choose correctly when individual votes are often correct and independent, and a modern Machine Learning concept called "Strength of Weak Learnability" (1990), which describes a method for converting a weak learning algorithm into one that achieves arbitrarily high accuracy and stands in the basis of Ensemble Learning.
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.
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.
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.
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.
