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Found 22 papers, 0 papers with code
Adaptive Learning for the Resource-Constrained Classification Problem
Resource-constrained classification tasks are common in real-world applications such as allocating tests for disease diagnosis, hiring decisions when filling a limited number of positions, and defect detection in manufacturing settings under a limited inspection budget.
JULIA: Joint Multi-linear and Nonlinear Identification for Tensor Completion
Tensor completion aims at imputing missing entries from a partially observed tensor.
A Novel Convergence Analysis for the Stochastic Proximal Point Algorithm
In this paper, we study the stochastic proximal point algorithm (SPPA) for general empirical risk minimization (ERM) problems as well as deep learning problems.
Stochastic Proximal Point Algorithm for Large-scale Nonconvex Optimization: Convergence, Implementation, and Application to Neural Networks
SPPA has been shown to converge faster and more stable than the celebrated stochastic gradient descent (SGD) algorithm, and its many variations, for convex problems.
Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems
To the best of our knowledge, this is the first time that first-order algorithms with polynomial per-iteration complexity and global sublinear rate are designed to find SOSPs of the important class of non-convex problems with linear constraints (almost surely).
Computing Large-Scale Matrix and Tensor Decomposition with Structured Factors: A Unified Nonconvex Optimization Perspective
The proposed article aims at offering a comprehensive tutorial for the computational aspects of structured matrix and tensor factorization.
Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms
The data deluge comes with high demands for data labeling.
SNAP: Finding Approximate Second-Order Stationary Solutions Efficiently for Non-convex Linearly Constrained Problems
This paper proposes low-complexity algorithms for finding approximate second-order stationary points (SOSPs) of problems with smooth non-convex objective and linear constraints.
Block-Randomized Stochastic Proximal Gradient for Low-Rank Tensor Factorization
In this work, we propose a stochastic optimization framework for large-scale CPD with constraints/regularizations.
Learning Nonlinear Mixtures: Identifiability and Algorithm
Linear mixture models have proven very useful in a plethora of applications, e. g., topic modeling, clustering, and source separation.
Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications
Perhaps a bit surprisingly, the understanding to its model identifiability---the major reason behind the interpretability in many applications such as topic mining and hyperspectral imaging---had been rather limited until recent years.
Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling
We present a new algorithm for identifying the transition and emission probabilities of a hidden Markov model (HMM) from the emitted data.
Kullback-Leibler Principal Component for Tensors is not NP-hard
We study the problem of nonnegative rank-one approximation of a nonnegative tensor, and show that the globally optimal solution that minimizes the generalized Kullback-Leibler divergence can be efficiently obtained, i. e., it is not NP-hard.
On Convergence of Epanechnikov Mean Shift
However, since the procedure involves non-smooth kernel density functions, the convergence behavior of Epanechnikov mean shift lacks theoretical support as of this writing---most of the existing analyses are based on smooth functions and thus cannot be applied to Epanechnikov Mean Shift.
On Identifiability of Nonnegative Matrix Factorization
In this letter, we propose a new identification criterion that guarantees the recovery of the low-rank latent factors in the nonnegative matrix factorization (NMF) model, under mild conditions.
Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm
In topic modeling, many algorithms that guarantee identifiability of the topics have been developed under the premise that there exist anchor words -- i. e., words that only appear (with positive probability) in one topic.
Robust Volume Minimization-Based Matrix Factorization for Remote Sensing and Document Clustering
This paper considers \emph{volume minimization} (VolMin)-based structured matrix factorization (SMF).
Tensor Decomposition for Signal Processing and Machine Learning
Tensors or {\em multi-way arrays} are functions of three or more indices $(i, j, k,\cdots)$ -- similar to matrices (two-way arrays), which are functions of two indices $(r, c)$ for (row, column).
Scalable and Flexible Multiview MAX-VAR Canonical Correlation Analysis
Generalized canonical correlation analysis (GCCA) aims at finding latent low-dimensional common structure from multiple views (feature vectors in different domains) of the same entities.
Joint Tensor Factorization and Outlying Slab Suppression with Applications
Convergence of the proposed algorithm is also easy to analyze under the framework of alternating optimization and its variants.
A Flexible and Efficient Algorithmic Framework for Constrained Matrix and Tensor Factorization
We propose a general algorithmic framework for constrained matrix and tensor factorization, which is widely used in signal processing and machine learning.
