inlier
Robust Model Reasoning and Fitting via Dual Sparsity Pursuit
In this paper, we contribute to solving a threefold problem: outlier rejection, true model reasoning and parameter estimation with a unified optimization modeling. To this end, we first pose this task as a sparse subspace recovering problem, to search a maximum of independent bases under an over-embedded data space. Then we convert the objective into a continuous optimization paradigm that estimates sparse solutions for both bases and errors. Wherein a fast and robust solver is proposed to accurately estimate the sparse subspace parameters and error entries, which is implemented by a proximal approximation method under the alternating optimization framework with the "optimal" sub-gradient descent. Extensive experiments regarding known and unknown model fitting on synthetic and challenging real datasets have demonstrated the superiority of our method against the stateof-the-art. We also apply our method to multi-class multi-model fitting and loop closure detection, and achieve promising results both in accuracy and efficiency. Code is released at: https://github.com/StaRainJ/DSP.
On Robustness of Kernel Clustering
Neural Information Processing Systems http://nips.cc/
EB-RANSAC: Random Sample Consensus based on Energy-Based Model
Yasuda, Muneki, Watanabe, Nao, Sekimoto, Kaiji
Random sample consensus (RANSAC), which is based on a repetitive sampling from a given dataset, is one of the most popular robust estimation methods. In this study, an energy-based model (EBM) for robust estimation that has a similar scheme to RANSAC, energy-based RANSAC (EB-RANSAC), is proposed. EB-RANSAC is applicable to a wide range of estimation problems similar to RANSAC. However, unlike RANSAC, EB-RANSAC does not require a troublesome sampling procedure and has only one hyperparameter. The effectiveness of EB-RANSAC is numerically demonstrated in two applications: a linear regression and maximum likelihood estimation.
Outlier Detection and Robust PCA Using a Convex Measure of Innovation
Neural Information Processing Systems http://nips.cc/
List-decodable Linear Regression
Sushrut Karmalkar, Adam Klivans, Pravesh Kothari
We give the first polynomial-time algorithm for robust regression in the listdecodable setting where an adversary can corrupt a greater than1/2 fraction ofexamples.
Greedy Sampling for Approximate Clustering in the Presence of Outliers
Aditya Bhaskara, Sharvaree Vadgama, Hong Xu
Neural Information Processing Systems http://nips.cc/
Checklist
Alldatausedispublic.] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they werechosen)? A.1 TrainingDetails In our experiments, the classifierfθ is a 8-layer MLP with 128 hidden dimensions per layer.
RobustandFully-DynamicCoresetfor Continuous-and-BoundedLearning(WithOutliers) Problems
Moreover, our robust coreset can be efficiently maintained in fullydynamic environment. To the best of our knowledge, this is the first robust and fully-dynamic coreset construction method for these optimization problems.