… (y))| ≤ δ) is called a (δ, k)-outlier embedding of X into Y2. When ϕ is an isometry (ie when δ
… outlier embedding. We are interested in efficient algorithms that compute outlier embeddings …

… first unsupervised deep architecture for outlier aware attributed network embedding. • We …
the idea of adversarial learning for outlier aware network embedding. To the best of our …

… such outliers while learning embeddings from the structure, we introduce the structural outlier
… The bigger the value of O1i, the more likely it is that node vi is an outlier, and lesser should …

… outlier embeddings into the Euclidean metric. Given a metric (X, dX) that admits a (k, c)-outlier
embedding into … k),O(c))-outlier embedding into ℓ2. In other words, our algorithm removes …

… of word embeddings for projecting literature submissions into high a dimensional space in
order to cluster similar papers, allocate papers to relevant experts as well as identify outlier …

… and the quality of the embedding with and without our outlier filtering. More precisely, we
synthesize ground-truth data by randomly sampling N points in ad dimensional hypercube, and …

… outliers which are omnipresent in currently available massive and heterogenous datasets. To
the best of our knowledge, systematically designing outlier-robust mean embedding … outlier …

… It is done by utilizing the Local Outlier Factor on document embeddings… Moreover, we
researched various distance metrics and … Results show that using the cosine distance metric in …

… 10% outliers in each dataset and the embedding vector of the t th graph is denoted as e t .
The center of embedding … Similar graphs have a short distance in an embedding space using …

… select embeddings with the smallest L2 distances. Once we have identified a set of ambiguous
ID embeddings, we synthesize outliers by … ID embedding s ∼ tG (zi,σ2I) where the s ∈ Ln …