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 method predicts binary matrix entries using empirical Bayes and low-rank structure.
A novel BMC model with nonconvex regularizers and accelerated proximal algorithm for binary matrix completion.
Paper proposes an efficient algorithm for nonnegative binary matrix factorization.
A matrix completion problem, which aims to recover a complete matrix from its partial observations, is one of the important problems in the machine learning field and has been studied actively. However, there is a discrepancy between the mainstream problem setting, which assumes continuous-valued observations, and some…
In this paper, we consider the matrix completion problem when the observations are one-bit measurements of some underlying matrix M, and in particular the observed samples consist only of ones and no zeros. This problem is motivated by modern applications such as recommender systems and social networks where only "like…
Study improves fractional posterior for 1-bit matrix completion.
A new method for 1-bit matrix completion that is faster and more accurate.
Characterizes uncertainty in low-rank matrix completion with noisy data.
New method predicts activity coefficients for binary mixtures without using physical descriptors.
Online algorithm for binary matrix completion with side information.
We consider the problem of matrix completion on an matrix. We introduce the problem of Interpretable Matrix Completion that aims to provide meaningful insights for the low-rank matrix using side information. We show that the problem can be reformulated as a binary convex optimization problem. We design Opt…
The paper proposes methods for predicting missing values in mixed data matrices.
Matrix completion aims to reconstruct a data matrix based on observations of a small number of its entries. Usually in matrix completion a single matrix is considered, which can be, for example, a rating matrix in recommendation system. However, in practical situations, data is often obtained from multiple sources whic…
New method estimates missingness probabilities for MNAR matrix completion.
Boolean matrix factorisation aims to decompose a binary data matrix into an approximate Boolean product of two low rank, binary matrices: one containing meaningful patterns, the other quantifying how the observations can be expressed as a combination of these patterns. We introduce the OrMachine, a probabilistic genera…
This paper introduces new methods to improve 1-bit matrix completion by considering cluster effects.
We consider the matrix completion problem of recovering a structured matrix from noisy and partial measurements. Recent works have proposed tractable estimators with strong statistical guarantees for the case where the underlying matrix is low--rank, and the measurements consist of a subset, either of the exact individ…
Due to challenging applications such as collaborative filtering, the matrix completion problem has been widely studied in the past few years. Different approaches rely on different structure assumptions on the matrix in hand. Here, we focus on the completion of a (possibly) low-rank matrix with binary entries, the so-c…
Improves matrix completion by exploiting biased observation patterns.
We consider a challenging multi-label classification problem where both feature matrix $\X$ and label matrix $\Y$ have missing entries. An existing method concatenated $\X$ and $\Y$ as $[\X; \Y]$ and applied a matrix completion (MC) method to fill the missing entries, under the assumption that $[\X; \Y]$ is of low-rank…
GMBL uses graph embedding to learn binary codes from multiple views for clustering.
We consider the problem of noisy 1-bit matrix completion under an exact rank constraint on the true underlying matrix . Instead of observing a subset of the noisy continuous-valued entries of a matrix , we observe a subset of noisy 1-bit (or binary) measurements generated according to a probabilistic model. W…
Paper develops a new weighted low-rank matrix approximation technique.
We consider analysis of relational data (a matrix), in which the rows correspond to subjects (e.g., people) and the columns correspond to attributes. The elements of the matrix may be a mix of real and categorical. Each subject and attribute is characterized by a latent binary feature vector, and an inferred matrix map…
Develops a fast BMF approach for binary matrices.
Matrix factorization is a key tool in data analysis; its applications include recommender systems, correlation analysis, signal processing, among others. Binary matrices are a particular case which has received significant attention for over thirty years, especially within the field of data mining. Dictionary learning …
Paper proposes algorithms for BMF using integer programming.
Federated learning approach for binary matrix factorization.
Reverse annealing boosts quantum matrix factorization performance.
BIND removes background noise from binary matrices, improving detection accuracy and fairness.
Single individual haplotyping is an NP-hard problem that emerges when attempting to reconstruct an organism's inherited genetic variations using data typically generated by high-throughput DNA sequencing platforms. Genomes of diploid organisms, including humans, are organized into homologous pairs of chromosomes that d…
New pruning technique reduces index size for DNNs.
Networked sensing, where the goal is to perform complex inference using a large number of inexpensive and decentralized sensors, has become an increasingly attractive research topic due to its applications in wireless sensor networks and internet-of-things. To reduce the communication, sensing and storage complexity, t…
Paper learns causal structures from data using gradient-based optimization.
The recently proposed SPARse Factor Analysis (SPARFA) framework for personalized learning performs factor analysis on ordinal or binary-valued (e.g., correct/incorrect) graded learner responses to questions. The underlying factors are termed "concepts" (or knowledge components) and are used for learning analytics (LA),…
New insights into identifying mixtures of product distributions using Hadamard extensions.
A Bayesian Boolean Matrix Factorization for cancer genomics
The study shows inner-product kernels behave similarly to binary kernels in high dimensions.
Many applications require recovering a ground truth low-rank matrix from noisy observations of the entries, which in practice is typically formulated as a weighted low-rank approximation problem and solved by non-convex optimization heuristics such as alternating minimization. In this paper, we provide provable recover…
The paper develops algorithms for Boolean matrix factorization using IP and heuristics.
Improves causal graph learning on dependent binary data.
BELIEF framework interprets GLMs using binary linear models.
Principal component analysis (PCA) for binary data, known as logistic PCA, has become a popular alternative to dimensionality reduction of binary data. It is motivated as an extension of ordinary PCA by means of a matrix factorization, akin to the singular value decomposition, that maximizes the Bernoulli log-likelihoo…
Unified approach optimizes neural network training for various metrics.
The study develops a supervised and unsupervised WTA model for sparse binary projections.
Unified framework for nonconvex matrix completion with linearly parameterized factors.
Binary data matrices can represent many types of data such as social networks, votes, or gene expression. In some cases, the analysis of binary matrices can be tackled with nonnegative matrix factorization (NMF), where the observed data matrix is approximated by the product of two smaller nonnegative matrices. In this …
The paper extends ternary algebra concepts using cube roots of unity.