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 test determines appropriate number of biclusters in relational data.
New biclustering algorithms for microarray data using Formal Concept Analysis.
RIn-Close_CVC2 improves biclustering efficiency by reducing memory usage.
Develops a non-parametric Dirichlet process method for probabilistic biclustering.
The H-score can bias bicluster comparisons, but a correction is provided.
In the biclustering problem, we seek to simultaneously group observations and features. While biclustering has applications in a wide array of domains, ranging from text mining to collaborative filtering, the problem of identifying structure in high dimensional genomic data motivates this work. In this context, biclust…
Being an unsupervised machine learning and data mining technique, biclustering and its multimodal extensions are becoming popular tools for analysing object-attribute data in different domains. Apart from conventional clustering techniques, biclustering is searching for homogeneous groups of objects while keeping their…
Sparse Convex Biclustering improves accuracy and robustness in high-dimensional datasets.
Robust biclustering method tackles heavy-tailed data issues.
New algorithm for biclustering with improved performance.
Exclusive row biclustering for gene expression data.
Improves convex biclustering for high-dimensional data.
Improved biclustering algorithm reduces memory usage and runtime.
Efficient biclustering of tensor data for identifying similar signal patterns over time.
Multimodal clustering is an unsupervised technique for mining interesting patterns in -adic binary relations or -mode networks. Among different types of such generalized patterns one can find biclusters and formal concepts (maximal bicliques) for 2-mode case, triclusters and triconcepts for 3-mode case, closed $n…
New biclustering algorithm in Hilbert spaces for complex data.
Identifying latent structure in large data matrices is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are locally co-regulated by shared biological mechanisms. To do this, we de…
Motivation: Modelling methods that find structure in data are necessary with the current large volumes of genomic data, and there have been various efforts to find subsets of genes exhibiting consistent patterns over subsets of treatments. These biclustering techniques have focused on one data source, often gene expres…
New sparse penalty improves biclustering for gene expression data.
In many situations it is desirable to identify clusters that differ with respect to only a subset of features. Such clusters may represent homogeneous subgroups of patients with a disease, such as cancer or chronic pain. We define a bicluster to be a submatrix U of a larger data matrix X such that the features and obse…
Perceptrons are neuronal devices capable of fully discriminating linearly separable classes. Although straightforward to implement and train, their applicability is usually hindered by non-trivial requirements imposed by real-world classification problems. Therefore, several approaches, such as kernel perceptrons, have…
Unified method for discovering biclusters and triclusters in longitudinal data.
The problem of biclustering consists of the simultaneous clustering of rows and columns of a matrix such that each of the submatrices induced by a pair of row and column clusters is as uniform as possible. In this paper we approximate the optimal biclustering by applying one-way clustering algorithms independently on t…
Biclustering structures in data matrices were first formalized in a seminal paper by John Hartigan (1972) where one seeks to cluster cases and variables simultaneously. Such structures are also prevalent in block modeling of networks. In this paper, we develop a unified theory for the estimation and completion of matri…
A biclustering algorithm finds dense disjoint subgraphs in weighted bipartite graphs.
Biclustering, the process of simultaneously clustering the rows and columns of a data matrix, is a popular and effective tool for finding structure in a high-dimensional dataset. Many biclustering procedures appear to work well in practice, but most do not have associated consistency guarantees. To address this shortco…
EBIC is a biclustering tool for big genomic data, achieving significant speedup.
New method for clustering bipartite networks achieves optimal performance.
In this paper we propose two new algorithms based on biclustering analysis, which can be used at the basis of a recommender system for educational orientation of Russian School graduates. The first algorithm was designed to help students make a choice between different university faculties when some of their preference…
Learning the "blocking" structure is a central challenge for high dimensional data (e.g., gene expression data). Recently, a sparse singular value decomposition (SVD) has been used as a biclustering tool to achieve this goal. However, this model ignores the structural information between variables (e.g., gene interacti…
AutoYara generates effective Yara rules faster than humans.
New computational lower bounds for clustering and related problems.
New group-sparse SVD models improve biclustering of gene expression data.
funLOCI identifies clusters in functional data.
Biarchetype analysis identifies extreme instances of observations and features.
Improved model for grouping nodes in bipartite networks.
The principal submatrix localization problem deals with recovering a principal submatrix of elevated mean in a large symmetric matrix subject to additive standard Gaussian noise. This problem serves as a prototypical example for community detection, in which the community corresponds to the …
We consider the problem of performing matrix completion with side information on row-by-row and column-by-column similarities. We build upon recent proposals for matrix estimation with smoothness constraints with respect to row and column graphs. We present a novel iterative procedure for directly minimizing an informa…
We provide a unified treatment of a broad class of noisy structure recovery problems, known as structured normal means problems. In this setting, the goal is to identify, from a finite collection of Gaussian distributions with different means, the distribution that produced some observed data. Recent work has studied s…
Many modern big data applications feature large scale in both numbers of responses and predictors. Better statistical efficiency and scientific insights can be enabled by understanding the large-scale response-predictor association network structures via layers of sparse latent factors ranked by importance. Yet sparsit…
Dyadic Data Prediction (DDP) is an important problem in many research areas. This paper develops a novel fully Bayesian nonparametric framework which integrates two popular and complementary approaches, discrete mixed membership modeling and continuous latent factor modeling into a unified Heterogeneous Matrix Factoriz…
Develops methods to analyze feature-outcome associations in subpopulations.
New test for latent block models to determine cluster numbers.
Identifies a sub-matrix with maximal sum in large data matrices.
SCC clusters data with supervising variables for better interpretation.
New spectral clustering method improves community detection in sparse networks.
Paper proposes ABDR for convex subspace clustering with adaptive block diagonal representation.
Divide-and-conquer method speeds sparse factorization for large matrices.