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Found 22 papers, 0 papers with code
Automated Data Denoising for Recommendation
In real-world scenarios, most platforms collect both large-scale, naturally noisy implicit feedback and small-scale yet highly relevant explicit feedback.
Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series
This paper presents a novel, closed-form, and data/computation efficient online anomaly detection algorithm for time-series data.
Provable Clustering of a Union of Linear Manifolds Using Optimal Directions
A novel theoretical study is presented which sheds light on the key performance factors of both algorithms (MFC/iPursuit) and it is shown that both algorithms can be robust to notable intersections between the span of clusters.
Non-Local Feature Aggregation on Graphs via Latent Fixed Data Structures
The proposed approach is used to design several novel global feature aggregation methods based on the choice of the LFDS.
Provable Data Clustering via Innovation Search
It is shown that in contrast to most of the existing methods which require the subspaces to be sufficiently incoherent with each other, Innovation Pursuit only requires the innovative components of the subspaces to be sufficiently incoherent with each other.
Closed-Form, Provable, and Robust PCA via Leverage Statistics and Innovation Search
In the application of Innovation Search for outlier detection, the directions of innovation were utilized to measure the innovation of the data points.
Outlier Detection and Data Clustering via Innovation Search
In this paper, we present a new discovery that the directions of innovation can be used to design a provable and strong robust (to outlier) PCA method.
Outlier Detection and Robust PCA Using a Convex Measure of Innovation
In other word, an outlier carries some innovation with respect to most of the other data points.
Graph Analysis and Graph Pooling in the Spatial Domain
The proposed approach leverages a spatial representation of the graph which makes the neural network aware of the differences between the nodes and also their locations in the graph.
DEEP GEOMETRICAL GRAPH CLASSIFICATION
In the second step, the GNN is applied to the point-cloud representation of the graph provided by the embedding method.
Scalable and Robust Community Detection with Randomized Sketching
Uniform random node sampling is shown to improve the computational complexity over clustering of the full graph when the cluster sizes are balanced.
Data Dropout in Arbitrary Basis for Deep Network Regularization
An important problem in training deep networks with high capacity is to ensure that the trained network works well when presented with new inputs outside the training dataset.
Innovation Pursuit: A New Approach to the Subspace Clustering Problem
Remarkably, the proposed approach can provably yield exact clustering even when the subspaces have significant intersections.
Coherence Pursuit: Fast, Simple, and Robust Subspace Recovery
To the best of our knowledge, this is the first provable robust PCA algorithm that is simultaneously non-iterative, can tolerate a large number of outliers and is robust to linearly dependent outliers.
Subspace Clustering via Optimal Direction Search
This letter presents a new spectral-clustering-based approach to the subspace clustering problem.
Spatial Random Sampling: A Structure-Preserving Data Sketching Tool
Random column sampling is not guaranteed to yield data sketches that preserve the underlying structures of the data and may not sample sufficiently from less-populated data clusters.
Low Rank Matrix Recovery with Simultaneous Presence of Outliers and Sparse Corruption
Our approach hinges on the sparse approximation of a sparsely corrupted column so that the sparse expansion of a column with respect to the other data points is used to distinguish a sparsely corrupted inlier column from an outlying data point.
Robust and Scalable Column/Row Sampling from Corrupted Big Data
Conventional sampling techniques fall short of drawing descriptive sketches of the data when the data is grossly corrupted as such corruptions break the low rank structure required for them to perform satisfactorily.
Coherence Pursuit: Fast, Simple, and Robust Principal Component Analysis
As inliers lie in a low dimensional subspace and are mostly correlated, an inlier is likely to have strong mutual coherence with a large number of data points.
Innovation Pursuit: A New Approach to Subspace Clustering
This paper presents a new approach dubbed Innovation Pursuit (iPursuit) to the problem of subspace clustering using a new geometrical idea whereby subspaces are identified based on their relative novelties.
Randomized Robust Subspace Recovery for High Dimensional Data Matrices
This paper explores and analyzes two randomized designs for robust Principal Component Analysis (PCA) employing low-dimensional data sketching.
High Dimensional Low Rank plus Sparse Matrix Decomposition
In this paper, a scalable subspace-pursuit approach that transforms the decomposition problem to a subspace learning problem is proposed.
