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Found 65 papers, 19 papers with code
Kernel Alignment for Unsupervised Feature Selection via Matrix Factorization
By doing so, our model can learn both linear and nonlinear similarity information and automatically generate the most appropriate kernel.
Benign overfitting in leaky ReLU networks with moderate input dimension
The problem of benign overfitting asks whether it is possible for a model to perfectly fit noisy training data and still generalize well.
Stochastic gradient descent for streaming linear and rectified linear systems with Massart noise
We propose SGD-exp, a stochastic gradient descent approach for linear and ReLU regressions under Massart noise (adversarial semi-random corruption model) for the fully streaming setting.
Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization
Block majorization-minimization (BMM) is a simple iterative algorithm for nonconvex optimization that sequentially minimizes a majorizing surrogate of the objective function in each block coordinate while the other block coordinates are held fixed.
Stratified-NMF for Heterogeneous Data
Non-negative matrix factorization (NMF) is an important technique for obtaining low dimensional representations of datasets.
Manifold Filter-Combine Networks
We introduce a class of manifold neural networks (MNNs) that we call Manifold Filter-Combine Networks (MFCNs), that aims to further our understanding of MNNs, analogous to how the aggregate-combine framework helps with the understanding of graph neural networks (GNNs).
Stochastic Natural Thresholding Algorithms
Sparse signal recovery is one of the most fundamental problems in various applications, including medical imaging and remote sensing.
Detecting and Mitigating Indirect Stereotypes in Word Embeddings
To evaluate this method, we perform a series of common tests and demonstrate that measures of bias in the word embeddings are reduced in exchange for minor reduction in the semantic quality of the embeddings.
Robust Tensor CUR Decompositions: Rapid Low-Tucker-Rank Tensor Recovery with Sparse Corruption
We study the tensor robust principal component analysis (TRPCA) problem, a tensorial extension of matrix robust principal component analysis (RPCA), that aims to split the given tensor into an underlying low-rank component and a sparse outlier component.
Linear Convergence of Reshuffling Kaczmarz Methods With Sparse Constraints
The Kaczmarz method (KZ) and its variants, which are types of stochastic gradient descent (SGD) methods, have been extensively studied due to their simplicity and efficiency in solving linear equation systems.
Neural Nonnegative Matrix Factorization for Hierarchical Multilayer Topic Modeling
We introduce a new method based on nonnegative matrix factorization, Neural NMF, for detecting latent hierarchical structure in data.
Randomized Kaczmarz in Adversarial Distributed Setting
Additionally, the efficiency of the proposed methods for solving convex problems is shown in simulations with the presence of adversaries.
Federated Gradient Matching Pursuit
In particular, federated learning (FL) provides such a solution to learn a shared model while keeping training data at local clients.
A Convergence Rate for Manifold Neural Networks
Moreover, in this work, the authors provide a numerical scheme for implementing such neural networks when the manifold is unknown and one only has access to finitely many sample points.
Continuous Semi-Supervised Nonnegative Matrix Factorization
Nonnegative matrix factorization can be used to automatically detect topics within a corpus in an unsupervised fashion.
Inference of Media Bias and Content Quality Using Natural-Language Processing
We then infer a ``media-bias chart'' of (bias, quality) coordinates for the media outlets by integrating the (bias, quality) measurements of the tweets of the media outlets.
Sketched Gaussian Model Linear Discriminant Analysis via the Randomized Kaczmarz Method
We present convergence guarantees for the sketched predictions on new data within a fixed number of iterations.
Automatic Infectious Disease Classification Analysis with Concept Discovery
Automatic infectious disease classification from images can facilitate needed medical diagnoses.
Matrix Completion with Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR Sampling
While uniform sampling has been widely studied in the matrix completion literature, CUR sampling approximates a low-rank matrix via row and column samples.
Geometric Scattering on Measure Spaces
Our proposed framework includes previous work on geometric scattering as special cases but also applies to more general settings such as directed graphs, signed graphs, and manifolds with boundary.
SP2: A Second Order Stochastic Polyak Method
We take a step further and develop a method for solving the interpolation equations that uses the local second-order approximation of the model.
The Manifold Scattering Transform for High-Dimensional Point Cloud Data
The manifold scattering transform is a deep feature extractor for data defined on a Riemannian manifold.
Distributed randomized Kaczmarz for the adversarial workers
Developing large-scale distributed methods that are robust to the presence of adversarial or corrupted workers is an important part of making such methods practical for real-world problems.
Semi-supervised Nonnegative Matrix Factorization for Document Classification
We propose new semi-supervised nonnegative matrix factorization (SSNMF) models for document classification and provide motivation for these models as maximum likelihood estimators.
Guided Semi-Supervised Non-negative Matrix Factorization on Legal Documents
Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets.
On audio enhancement via online non-negative matrix factorization
We propose a method for noise reduction, the task of producing a clean audio signal from a recording corrupted by additive noise.
A Generalized Hierarchical Nonnegative Tensor Decomposition
Hierarchical NTF (HNTF) methods have been proposed, however these methods do not naturally generalize their matrix-based counterparts.
Fast Robust Tensor Principal Component Analysis via Fiber CUR Decomposition
We study the problem of tensor robust principal component analysis (TRPCA), which aims to separate an underlying low-multilinear-rank tensor and a sparse outlier tensor from their sum.
Analysis of Legal Documents via Non-negative Matrix Factorization Methods
The California Innocence Project (CIP), a clinical law school program aiming to free wrongfully convicted prisoners, evaluates thousands of mails containing new requests for assistance and corresponding case files.
Mode-wise Tensor Decompositions: Multi-dimensional Generalizations of CUR Decompositions
Low rank tensor approximation is a fundamental tool in modern machine learning and data science.
Robust CUR Decomposition: Theory and Imaging Applications
Additionally, we consider hybrid randomized and deterministic sampling methods which produce a compact CUR decomposition of a given matrix, and apply this to video sequences to produce canonical frames thereof.
Neural Nonnegative CP Decomposition for Hierarchical Tensor Analysis
We propose a new hierarchical nonnegative CANDECOMP/PARAFAC (CP) decomposition (hierarchical NCPD) model and a training method, Neural NCPD, for performing hierarchical topic modeling on multi-modal tensor data.
Applications of Online Nonnegative Matrix Factorization to Image and Time-Series Data
Online nonnegative matrix factorization (ONMF) is a matrix factorization technique in the online setting where data are acquired in a streaming fashion and the matrix factors are updated each time.
On a Guided Nonnegative Matrix Factorization
Fully unsupervised topic models have found fantastic success in document clustering and classification.
Semi-supervised NMF Models for Topic Modeling in Learning Tasks
We propose several new models for semi-supervised nonnegative matrix factorization (SSNMF) and provide motivation for SSNMF models as maximum likelihood estimators given specific distributions of uncertainty.
Ranked #13 on
Text Classification
on 20NEWS
Sparseness-constrained Nonnegative Tensor Factorization for Detecting Topics at Different Time Scales
Further, we propose quantitative ways to measure the topic length and demonstrate the ability of S-NCPD (as well as its online variant) to discover short and long-lasting temporal topics in a controlled manner in semi-synthetic and real-world data including news headlines.
Online nonnegative CP-dictionary learning for Markovian data
We prove that our algorithm converges almost surely to the set of stationary points of the objective function under the hypothesis that the sequence of data tensors is generated by an underlying Markov chain.
COVID-19 Literature Topic-Based Search via Hierarchical NMF
A dataset of COVID-19-related scientific literature is compiled, combining the articles from several online libraries and selecting those with open access and full text available.
Feature Selection on Lyme Disease Patient Survey Data
We first analyze the general performance of the model and then identify the most important features for predicting participant answers to GROC.
Random Vector Functional Link Networks for Function Approximation on Manifolds
We provide a (corrected) rigorous proof that the Igelnik and Pao construction is a universal approximator for continuous functions on compact domains, with approximation error decaying asymptotically like $O(1/\sqrt{n})$ for the number $n$ of network nodes.
COVID-19 Time-series Prediction by Joint Dictionary Learning and Online NMF
One of the main sources of difficulty is that a very limited amount of daily COVID-19 case data is available, and with few exceptions, the majority of countries are currently in the "exponential spread stage," and thus there is scarce information available which would enable one to predict the phase transition between spread and containment.
On Large-Scale Dynamic Topic Modeling with Nonnegative CP Tensor Decomposition
There is currently an unprecedented demand for large-scale temporal data analysis due to the explosive growth of data.
Topic-aware chatbot using Recurrent Neural Networks and Nonnegative Matrix Factorization
We propose a novel model for a topic-aware chatbot by combining the traditional Recurrent Neural Network (RNN) encoder-decoder model with a topic attention layer based on Nonnegative Matrix Factorization (NMF).
Online matrix factorization for Markovian data and applications to Network Dictionary Learning
As the main application, by combining online non-negative matrix factorization and a recent MCMC algorithm for sampling motifs from networks, we propose a novel framework of Network Dictionary Learning, which extracts ``network dictionary patches' from a given network in an online manner that encodes main features of the network.
Adaptive Sketch-and-Project Methods for Solving Linear Systems
We present new adaptive sampling rules for the sketch-and-project method for solving linear systems.
Numerical Analysis Numerical Analysis 15A06, 15B52, 65F10, 68W20, 65N75, 65Y20, 68Q25, 68W40, 90C20
Iterative Hard Thresholding for Low CP-rank Tensor Models
In this paper, we utilize the same tensor version of the Restricted Isometry Property (RIP) to extend these results for tensors with low CANDECOMP/PARAFAC (CP) rank.
Bias of Homotopic Gradient Descent for the Hinge Loss
Gradient descent is a simple and widely used optimization method for machine learning.
Data-driven Algorithm Selection and Parameter Tuning: Two Case studies in Optimization and Signal Processing
Machine learning algorithms typically rely on optimization subroutines and are well-known to provide very effective outcomes for many types of problems.
Matrix Completion With Selective Sampling
Matrix completion is a classical problem in data science wherein one attempts to reconstruct a low-rank matrix while only observing some subset of the entries.
An iterative method for classification of binary data
Building on a recently designed simple framework for classification using binary data, we demonstrate that one can improve classification accuracy of this approach through iterative applications whose output serves as input to the next application.
Tribracket Modules
We provide examples to illustrate the computation of the invariant and show that the enhancement is proper.
Geometric Topology Quantum Algebra 57M27, 57M25
Hierarchical Classification using Binary Data
In classification problems, especially those that categorize data into a large number of classes, the classes often naturally follow a hierarchical structure.
Analysis of Fast Structured Dictionary Learning
Sparsity-based models and techniques have been exploited in many signal processing and imaging applications.
Analysis of Fast Alternating Minimization for Structured Dictionary Learning
Alternating minimization algorithms have been particularly popular in dictionary or transform learning.
Matrix Completion for Structured Observations
We propose adjusting the standard nuclear norm minimization strategy for matrix completion to account for such structural differences between observed and unobserved entries by regularizing the values of the unobserved entries.
Simple Classification using Binary Data
Binary, or one-bit, representations of data arise naturally in many applications, and are appealing in both hardware implementations and algorithm design.
Boltzmann Enhancements of Biquasile Counting Invariants
In this paper, we build on the biquasiles and dual graph diagrams introduced in arXiv:1610. 06969.
Geometric Topology Quantum Algebra 57M25, 57M27
An Asynchronous Parallel Approach to Sparse Recovery
Asynchronous algorithms are often studied to solve optimization problems where the cost function takes the form $\sum_{i=1}^M f_i(x)$, with a common assumption that each $f_i$ is sparse; that is, each $f_i$ acts only on a small number of components of $x\in\mathbb{R}^n$.
Constrained adaptive sensing
Suppose that we wish to estimate a vector $\mathbf{x} \in \mathbb{C}^n$ from a small number of noisy linear measurements of the form $\mathbf{y} = \mathbf{A x} + \mathbf{z}$, where $\mathbf{z}$ represents measurement noise.
Information Theory Information Theory
Improving Image Clustering using Sparse Text and the Wisdom of the Crowds
We propose a method to improve image clustering using sparse text and the wisdom of the crowds.
A Comparison of Clustering and Missing Data Methods for Health Sciences
In this paper, we compare and analyze clustering methods with missing data in health behavior research.
Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm
Furthermore, we show how reweighting the sampling distribution (i. e. importance sampling) is necessary in order to further improve convergence, and obtain a linear dependence in the average smoothness, dominating previous results.
Near-optimal compressed sensing guarantees for total variation minimization
Consider the problem of reconstructing a multidimensional signal from an underdetermined set of measurements, as in the setting of compressed sensing.
Paved with Good Intentions: Analysis of a Randomized Block Kaczmarz Method
The block Kaczmarz method is an iterative scheme for solving overdetermined least-squares problems.
Numerical Analysis Numerical Analysis 65F10, 65F20, 68W20, 41A65
Acceleration of Randomized Kaczmarz Method via the Johnson-Lindenstrauss Lemma
The Kaczmarz method is an algorithm for finding the solution to an overdetermined consistent system of linear equations Ax=b by iteratively projecting onto the solution spaces.
Numerical Analysis
