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Found 9 papers, 0 papers with code
Identifiable Solutions to Foreground Signature Extraction from Hyperspectral Images in an Intimate Mixing Scenario
The problem of foreground material signature extraction in an intimate (nonlinear) mixing setting is considered.
On Asymptotic Linear Convergence of Projected Gradient Descent for Constrained Least Squares
This manuscript presents a unified framework for the local convergence analysis of projected gradient descent in the context of constrained least squares.
A Closed-Form Bound on the Asymptotic Linear Convergence of Iterative Methods via Fixed Point Analysis
In this letter, we take into account the effect of the first-order approximation error and present a closed-form bound on the convergence in terms of the number of iterations required for the distance between the iterate and the limit point to reach an arbitrarily small fraction of the initial distance.
Active Learning in Incomplete Label Multiple Instance Multiple Label Learning
Due to the partial availability of bag-level labels, we focus on the incomplete-label MIML setting for the proposed active learning approach.
Exact Linear Convergence Rate Analysis for Low-Rank Symmetric Matrix Completion via Gradient Descent
Factorization-based gradient descent is a scalable and efficient algorithm for solving low-rank matrix completion.
Weakly-supervised Dictionary Learning
The performance of the proposed model is demonstrated on both synthetic and real-world data.
Confidence-Constrained Maximum Entropy Framework for Learning from Multi-Instance Data
In this approach each bag is represented as a distribution using the principle of ME.
Dynamic Programming for Instance Annotation in Multi-instance Multi-label Learning
To reduce labeling cost, instead of labeling every instance, a group of instances (bag) is labeled by a single bag label.
Novelty Detection Under Multi-Instance Multi-Label Framework
Contrary to the common assumption in MIML that each instance in a bag belongs to one of the known classes, in novelty detection, we focus on the scenario where bags may contain novel-class instances.
