Recent advances in neuroscience and in the technology of functional magnetic resonance imaging (fMRI) and electro-encephalography (EEG) have propelled a growing interest in brain-network clustering via time-series analysis. Notwithstanding, most of the brain-network clustering methods revolve around state clustering an…
On-device research index
arXiv research
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.
168,695 papers · 148 categories
Trend · papers per month
48 results for “state clustering”
This paper clusters networks with annotated time-series data using kernel-ARMA and Grassmannian geometry.
problem Clustering networks with annotated time-series data, including state, node, and subnetwork clustering.
method Extract features from time-series data using kernel-ARMA, map onto Grassmannian, and cluster using Riemannian geometry.
result The proposed framework outperforms state-of-the-art clustering schemes on brain-network data.
Improves clustering fairness by learning fair clusters adaptively.
problem Fairness in deep clustering, especially for protected status variables.
method Formulates group-level fairness as ILP, integrates into discriminative deep clustering, refines learning algorithm.
result Consistently outperforms fair clustering algorithms on real-world datasets.
Clusters of financial market states identified over 2006-2019.
problem Understanding the statistical properties of financial markets.
method Clustering analysis of correlation matrices constructed from sliding epochs.
result Financial markets can be classified into distinct states with transitions indicating precursors to catastrophic events.
RECODE uses clustering and embedding to track state visitation counts in RL.
problem Efficient novelty-based exploration in nonstationary RL environments.
method Non-parametric clustering, online density estimation, inverse dynamics loss.
result RECODE achieves state-of-the-art performance in challenging RL tasks.
A new asset allocation model uses Markov states from clustered efficient frontier coefficients.
problem Characterizing market regimes using efficient frontiers for better asset allocation.
method Hierarchical clustering of monthly efficient frontier coefficients to define states, then a Markov process on these states for portfolio optimization.
result The model significantly outperforms benchmark portfolios empirically.
We present a scheme for online, unsupervised state discovery and detection from streaming, multi-featured, asynchronous data in high-frequency financial markets. Online feature correlations are computed using an unbiased, lossless Fourier estimator. A high-speed maximum likelihood clustering algorithm is then used to f…
We propose the application of a high-speed maximum likelihood clustering algorithm to detect temporal financial market states, using correlation matrices estimated from intraday market microstructure features. We first determine the ex-ante intraday temporal cluster configurations to identify market states, and then st…
A new clustering algorithm considers data smoothness for better performance.
problem Clustering multi-scale data with varying cluster densities.
method Divide objects into tiny clusters, cluster centers form smooth graphs.
result Significantly outperforms state-of-the-art clustering algorithms.
Clustering is ubiquitous in data analysis, including analysis of time series. It is inherently subjective: different users may prefer different clusterings for a particular dataset. Semi-supervised clustering addresses this by allowing the user to provide examples of instances that should (not) be in the same cluster. …
Proposes ICC method for dynamic portfolio optimization.
problem Non-stationarity in market conditions makes traditional portfolio optimization ineffective.
method Inverse Covariance Clustering (ICC) to identify market states and integrate into dynamic optimization.
result ICC-PO generates portfolios with higher Sharpe Ratios and greater robustness.
State space partitioning based on constrained spectral clustering for block particle filteringstat.ML
A new method for state space partitioning in block particle filtering reduces bias and variance.
problem Overcoming the curse of dimensionality in non-linear, non-Gaussian state space estimation.
method Formulates state space partitioning as a clustering problem and uses spectral clustering with constraints.
result The proposed method effectively groups correlated state variables into smaller blocks, reducing bias and variance.
Robust feature-weighted jump models for time-dependent clustering
problem Temporal clustering
method Robust feature-weighted jump model
result Accurate recovery of true cluster sequence and feature identification
Exploration strategy design is one of the challenging problems in reinforcement learning~(RL), especially when the environment contains a large state space or sparse rewards. During exploration, the agent tries to discover novel areas or high reward~(quality) areas. In most existing methods, the novelty and quality in …
Mixed datasets consist of both numeric and categorical attributes. Various k-means-based clustering algorithms have been developed for these datasets. Generally, these algorithms use random partition as a starting point, which tends to produce different clustering results for different runs. In this paper, we propose, …
This paper presents a new approach to non-parametric cluster analysis called Adaptive Weights Clustering (AWC). The idea is to identify the clustering structure by checking at different points and for different scales on departure from local homogeneity. The proposed procedure describes the clustering structure in term…
Dissecting financial markets: Sectors and statescond-mat.stat-mech
By analyzing a large data set of daily returns with data clustering technique, we identify economic sectors as clusters of assets with a similar economic dynamics. The sector size distribution follows Zipf's law. Secondly, we find that patterns of daily market-wide economic activity cluster into classes that can be ide…
Proposes a constraint for deep clustering to handle both simple and complex topologies.
problem Limited prior knowledge for deep clustering methods to perform well on complex topologies.
method Introduces a constraint using symmetric InfoNCE to enhance deep clustering performance.
result The constraint improves deep clustering methods' performance on both simple and complex topologies.
This paper studies how to find compact state embeddings from high-dimensional Markov state trajectories, where the transition kernel has a small intrinsic rank. In the spirit of diffusion map, we propose an efficient method for learning a low-dimensional state embedding and capturing the process's dynamics. This idea a…
Cluster analysis aims at separating patients into phenotypically heterogenous groups and defining therapeutically homogeneous patient subclasses. It is an important approach in data-driven disease classification and subtyping. Acute coronary syndrome (ACS) is a syndrome due to sudden decrease of coronary artery blood f…
BanditPAM clusters data faster than traditional methods.
problem Efficiently clustering large datasets with arbitrary distance metrics.
method Inspired by multi-armed bandits, reduces PAM complexity from to .
result Matches state-of-the-art clustering loss with up to 4x faster results and 200x fewer distance computations.
Graph clustering remains challenging for GNNs, but a new method improves performance.
problem Graph clustering is difficult for GNNs, especially in noisy data.
method Developed Deep Modularity Networks (DMoN) inspired by modularity measure.
result DMoN produces high-quality clusters with over 40% improvement over other methods.
A framework for forecasting high-dimensional time-series data using clustering.
problem Forecasting high-dimensional time-series data with intra-cluster similarity.
method Three-stage framework: univariate time series parameter estimation, clustering, multivariate time series parameter computation.
result Framework achieves state-of-the-art results on benchmark datasets, sometimes outperforming deep-learning-based approaches.
CW-EDMD improves prediction accuracy by learning local Koopman models for different state-space regions.
problem Inefficient global Koopman operator approximation for distinct local dynamics.
method Cluster-Weighted EDMD (CW-EDMD) learns a soft phase-space partition and per-cluster EDMD operators using EM objective.
result CW-EDMD significantly reduces prediction errors across various systems and configurations.
Transform learning improves K-means clustering for document analysis.
problem Improving K-means clustering for document analysis.
method Embedding K-means clustering loss into transform learning framework and solving jointly using ADMM.
result Improves over state-of-the-art in document clustering.
Most existing approaches to clustering gene expression time course data treat the different time points as independent dimensions and are invariant to permutations, such as reversal, of the experimental time course. Approaches utilizing HMMs have been shown to be helpful in this regard, but are hampered by having to ch…
This paper presents a new multitask learning framework that learns a shared representation among the tasks, incorporating both task and feature clusters. The jointly-induced clusters yield a shared latent subspace where task relationships are learned more effectively and more generally than in state-of-the-art multitas…
Framework clusters noisy MTS with robust fuzzy clustering, improving accuracy over existing methods.
problem Challenges in clustering multivariate time series due to non-stationary dependencies, noise, and state boundaries.
method Spectral fuzzy clustering using Kendall's tau-based canonical coherence for frequency-specific monotonic relationships.
result Framework outperforms existing methods in clustering noisy, high-dimensional MTS.
Integrates VAEs into EM for deep clustering and generation.
problem Clustering and generating new samples from complex distributions.
method Combines VAEs and EM, updating model parameters and refining cluster assignments.
result Superior clustering performance on MNIST and FashionMNIST.
A clustering may be considered as fair on pre-specified sensitive attributes if the proportions of sensitive attribute groups in each cluster reflect that in the dataset. In this paper, we consider the task of fair clustering for scenarios involving multiple multi-valued or numeric sensitive attributes. We propose a fa…
This paper presents a new fuzzy k-means algorithm for the clustering of high-dimensional data in various subspaces. Since high-dimensional data, some features might be irrelevant and relevant but may have different significance in the clustering process. For better clustering, it is crucial to incorporate the contribut…
In this paper we propose a Deep Autoencoder MIxture Clustering (DAMIC) algorithm based on a mixture of deep autoencoders where each cluster is represented by an autoencoder. A clustering network transforms the data into another space and then selects one of the clusters. Next, the autoencoder associated with this clust…
Recurrent neural networks trained on regular languages exhibit stable states that can recover from noise.
problem Stability of internal states in recurrent neural networks trained on regular languages.
method Empirical study with analysis of network activation and transitions between states.
result Recurrent neural networks trained on regular languages can recover from random perturbations and maintain stable states.
Clustering is a fundamental unsupervised learning approach. Many clustering algorithms -- such as -means -- rely on the euclidean distance as a similarity measure, which is often not the most relevant metric for high dimensional data such as images. Learning a lower-dimensional embedding that can better reflect the …
SimpleMKKM improves multi-kernel clustering efficiency.
problem Efficient multi-kernel clustering.
method Re-formulated minimization-maximization problem into a smooth minimization, solved with gradient descent.
result Outperforms state-of-the-art multi-kernel clustering alternatives.
DIGRAC clusters directed graphs using flow imbalance, outperforming existing methods.
problem Clustering directed networks without label supervision.
method DIGRAC uses a graph neural network with a novel imbalance loss for directed flow imbalance.
result DIGRAC outperforms 10 state-of-the-art methods on directed graph clustering.
S3VDC improves DC methods for scalability, stability, and simplicity.
problem Poor scalability, instability, and lack of simplicity in DC methods.
method Four algorithmic improvements: initial -training, periodic -annealing, mini-batch GMM initialization, and inverse min-max transform. S3VDC incorporates all improvements.
result S3VDC outperforms state-of-the-art methods on benchmark and industrial datasets.
New method clusters multi-view data by squeezing hybrid knowledge.
problem Removal of redundant information and fusion of multi-view features.
method Low-rank subspace multi-view clustering with adaptive graph regularization.
result Our method outperforms state-of-the-art algorithms on multi-view benchmarks.
Efficiently clusters millions of data points with learnable subspace clustering.
problem Large-scale subspace clustering with high-dimensional data.
method Develops a learnable subspace clustering paradigm using a parametric function and RPCM.
result Outperforms state-of-the-art methods in efficiency and effectiveness.
Clusters cryptocurrency market states via cross correlation analysis.
problem Analyse cryptocurrency market dynamics.
method Cross correlation structure analysis over 5 years.
result Cryptocurrency market clusters into 4 states.
The mixture models have become widely used in clustering, given its probabilistic framework in which its based, however, for modern databases that are characterized by their large size, these models behave disappointingly in setting out the model, making essential the selection of relevant variables for this type of cl…
Proposes SDCN to integrate structural information into deep clustering.
problem Lack of attention to structural information in representation learning for clustering.
method Designs a delivery operator to transfer autoencoder representations to GCN layers and uses a dual self-supervised mechanism.
result SDCN consistently outperforms state-of-the-art techniques in clustering tasks.
This paper considers networks where relationships between nodes are represented by directed dissimilarities. The goal is to study methods for the determination of hierarchical clusters, i.e., a family of nested partitions indexed by a connectivity parameter, induced by the given dissimilarity structures. Our constructi…
DUET enhances multivariate time series forecasting by clustering time and channels.
problem Heterogeneous temporal patterns and complex channel correlations in multivariate time series.
method DUET uses dual clustering on temporal and channel dimensions to handle these challenges.
result DUET achieves state-of-the-art performance on 25 real-world datasets.
Cluster analysis is widely used in the areas of machine learning and data mining. Fuzzy clustering is a particular method that considers that a data point can belong to more than one cluster. Fuzzy clustering helps obtain flexible clusters, as needed in such applications as text categorization. The performance of a clu…
Ideas from the image processing literature have recently motivated a new set of clustering algorithms that rely on the concept of total variation. While these algorithms perform well for bi-partitioning tasks, their recursive extensions yield unimpressive results for multiclass clustering tasks. This paper presents a g…
Spectral clustering is one of the most effective clustering approaches that capture hidden cluster structures in the data. However, it does not scale well to large-scale problems due to its quadratic complexity in constructing similarity graphs and computing subsequent eigendecomposition. Although a number of methods h…
Proposes MFPC for cross-manifold clustering.
problem Cross-manifold clustering challenges traditional methods.
method Multiple Flat Projections Clustering (MFPC).
result MFPC distinguishes cross-manifold clusters from projections.