A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New bounds improve linkage methods for clustering, distinguishing complete-link from single-link.
Study of Legendrian links using Floer theory and cluster varieties.
The paper connects Legendrian links to cluster theory and exact Lagrangian fillings.
New link polynomials linked to cluster theory.
Semi-supervised clustering aims to introduce prior knowledge in the decision process of a clustering algorithm. In this paper, we propose a novel semi-supervised clustering algorithm based on the information-maximization principle. The proposed method is an extension of a previous unsupervised information-maximization …
The paper connects Legendrian links to cluster algebras via microlocal methods.
S3C2 uses Siamese networks for semi-supervised clustering with pairwise constraints.
We present a novel algorithm, called Links, designed to perform online clustering on unit vectors in a high-dimensional Euclidean space. The algorithm is appropriate when it is necessary to cluster data efficiently as it streams in, and is to be contrasted with traditional batch clustering algorithms that have access t…
Motivated by social balance theory, we develop a theory of link classification in signed networks using the correlation clustering index as measure of label regularity. We derive learning bounds in terms of correlation clustering within three fundamental transductive learning settings: online, batch and active. Our mai…
Paper proposes a new method to improve clustering ensemble performance.
We propose a method to compute complex volume of 2-bridge link complements. Our construction sheds light on a relationship between cluster variables with coefficients and canonical decompositions of link complements.
The clustering ensemble technique aims to combine multiple clusterings into a probably better and more robust clustering and has been receiving an increasing attention in recent years. There are mainly two aspects of limitations in the existing clustering ensemble approaches. Firstly, many approaches lack the ability t…
We consider the problem of decentralized clustering and estimation over multi-task networks, where agents infer and track different models of interest. The agents do not know beforehand which model is generating their own data. They also do not know which agents in their neighborhood belong to the same cluster. We prop…
Study links and quivers, proving polynomial equality conjecture.
Constrained clustering has been well-studied for algorithms such as -means and hierarchical clustering. However, how to satisfy many constraints in these algorithmic settings has been shown to be intractable. One alternative to encode many constraints is to use spectral clustering, which remains a developing area. I…
We prove the existence of Lagrangian fillings for -type Legendrian links.
Proposes clustering as a new evaluation method for clinical knowledge embedding.
Infinite hierarchical contrastive clustering identifies personal environments linked to health outcomes.
An important form of prior information in clustering comes in form of cannot-link and must-link constraints. We present a generalization of the popular spectral clustering technique which integrates such constraints. Motivated by the recently proposed -spectral clustering for the unconstrained problem, our method is…
Constructs Lagrangian skeleta for curve singularities.
Method detects and locates eavesdropping in optical links.
Geometric framework links clustering accuracy to structural recovery.
Subspace sparse coding (SSC) algorithms have proven to be beneficial to clustering problems. They provide an alternative data representation in which the underlying structure of the clusters can be better captured. However, most of the research in this area is mainly focused on enhancing the sparse coding part of the p…
Many interesting spaces --- including all positroid strata and wild character varieties --- are moduli of constructible sheaves on a surface with microsupport in a Legendrian link. We show that the existence of cluster structures on these spaces may be deduced in a uniform, systematic fashion by constructing and taking…
The paper proves there are many Lagrangian fillings for Legendrian links of affine type.
Although many successful ensemble clustering approaches have been developed in recent years, there are still two limitations to most of the existing approaches. First, they mostly overlook the issue of uncertain links, which may mislead the overall consensus process. Second, they generally lack the ability to incorpora…
The paper examines node2vec embeddings for community detection in networks.
We present a new and very concrete connection between cluster algebras and knot theory. This connection is being made via continued fractions and snake graphs. It is known that the class of 2-bridge knots and links is parametrized by continued fractions, and it has recently been shown that one can associate to each con…
Jones polynomials derived from K-theory of a cluster algebra.
New algorithm clusters data and learns kernels without relaxing constraints.
Nonnegative Matrix Factorization (NMF) was first introduced as a low-rank matrix approximation technique, and has enjoyed a wide area of applications. Although NMF does not seem related to the clustering problem at first, it was shown that they are closely linked. In this report, we provide a gentle introduction to clu…
Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network into clusters; the probability of a link between any two nodes then depends only on their cluster assignment. Currently available models can b…
We study constrained clustering, where constraints guide the clustering process. In existing works, two categories of constraints have been widely explored, namely pairwise and cardinality constraints. Pairwise constraints enforce the cluster labels of two instances to be the same (must-link constraints) or different (…
New model integrates community and link clustering for network data.
In Stochastic blockmodels, which are among the most prominent statistical models for cluster analysis of complex networks, clusters are defined as groups of nodes with statistically similar link probabilities within and between groups. A recent extension by Karrer and Newman incorporates a node degree correction to mod…
New model predicts links in community-based networks robustly.
Proposes a method to improve hierarchical clustering using set-level structural priors.
Study resolves conjecture linking two algebraic structures on surfaces.
Clustering is a widely used unsupervised learning method for finding structure in the data. However, the resulting clusters are typically presented without any guarantees on their robustness; slightly changing the used data sample or re-running a clustering algorithm involving some stochastic component may lead to comp…
This paper defines several algebras associated to an oriented surface with a finite set of marked points on the boundary. The first is the skein algebra , which is spanned by links in the surface which are allowed to have endpoints at the marked points, modulo several locally defined relations. The product…
Graph InfoClust learns node representations by capturing cluster-level information, improving graph mining tasks.
The study finds many Lagrangian fillings for Legendrian links of specific types.
EAP clusters evolving data, promoting temporal smoothness and automatic cluster tracking.
Subspace clustering aims to cluster unlabeled data that lies in a union of low-dimensional linear subspaces. Deep subspace clustering approaches based on auto-encoders have become very popular to solve subspace clustering problems. However, the training of current deep methods converges slowly, which is much less effic…
Data-driven methods link graphon limits to random walks and spectral clustering.
This paper studies posets associated with link diagrams and their algebraic properties.
We use statistically validated networks, a recently introduced method to validate links in a bipartite system, to identify clusters of investors trading in a financial market. Specifically, we investigate a special database allowing to track the trading activity of individual investors of the stock Nokia. We find that …
GAP learns node representations by attending to different parts of its neighborhood.