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.
SRMD uses random features for efficient time-frequency analysis.
Study on signal-plus-noise decomposition in nonlinear spiked random matrices.
Generalizes PCA and ICA for continuous-time signals using neural networks.
High throughput biomedical measurements normally capture multiple overlaid biologically relevant signals and often also signals representing different types of technical artefacts like e.g. batch effects. Signal identification and decomposition are accordingly main objectives in statistical biomedical modeling and data…
VDA improves disentanglement of latent representations in complex signals.
New method extracts joint and individual signals from multi-view data.
This paper presents a novel signal compression algorithm based on the Blaschke unwinding adaptive Fourier decomposition (AFD). The Blaschke unwinding AFD is a newly developed signal decomposition theory. It utilizes the Nevanlinna factorization and the maximal selection principle in each decomposition step, and achieve…
The measurement and analysis of Electrodermal Activity (EDA) offers applications in diverse areas ranging from market research, to seizure detection, to human stress analysis. Unfortunately, the analysis of EDA signals is made difficult by the superposition of numerous components which can obscure the signal informatio…
This paper proposes a subspace decomposition method based on an over-complete dictionary in sparse representation, called "Sparse Signal Subspace Decomposition" (or 3SD) method. This method makes use of a novel criterion based on the occurrence frequency of atoms of the dictionary over the data set. This criterion, wel…
Unified deep learning approach for time series forecasting using VMD-CNN-LSTM.
We propose a greedy variational method for decomposing a non-negative multivariate signal as a weighted sum of Gaussians, which, borrowing the terminology from statistics, we refer to as a Gaussian mixture model. Notably, our method has the following features: (1) It accepts multivariate signals, i.e. sampled multivari…
New algorithm for decomposing multidimensional, non-stationary signals.
Enhances forecasting of complex systems using FKMD.
Paper tackles GAN instability in audio and speech signals using a new similarity metric.
In this paper, we study a polynomial decomposition model that arises in problems of system identification, signal processing and machine learning. We show that this decomposition is a special case of the X-rank decomposition --- a powerful novel concept in algebraic geometry that generalizes the tensor CP decomposition…
Proposes a Bayesian approach for integrating multiple linked matrices.
DeepTensor uses deep networks to efficiently decompose tensors with improved performance and robustness.
New method uses tensor decomposition to improve noise reduction in machine fault detection.
Graphical notation simplifies tensor operations and decompositions.
Proposes a new graph trend filtering model for inhomogeneous graph signals.
Decomposing tensors into orthogonal factors is a well-known task in statistics, machine learning, and signal processing. We study orthogonal outer product decompositions where the factors in the summands in the decomposition are required to be orthogonal across summands, by relating this orthogonal decomposition to the…
We introduce Contrastive Multivariate Singular Spectrum Analysis, a novel unsupervised method for dimensionality reduction and signal decomposition of time series data. By utilizing an appropriate background dataset, the method transforms a target time series dataset in a way that evinces the sub-signals that are enhan…
We propose the product-of-filters (PoF) model, a generative model that decomposes audio spectra as sparse linear combinations of "filters" in the log-spectral domain. PoF makes similar assumptions to those used in the classic homomorphic filtering approach to signal processing, but replaces hand-designed decompositions…
This paper studies the effect of discretizing the parametrization of a dictionary used for Matching Pursuit decompositions of signals. Our approach relies on viewing the continuously parametrized dictionary as an embedded manifold in the signal space on which the tools of differential (Riemannian) geometry can be appli…
A new diffusion model improves time-series forecasting by preserving seasonal patterns.
Study evaluates thresholds for removing noise from DNN weights using random matrix theory.
BankGCN improves graph convolution networks by handling multi-channel signals with adaptive filter banks.
This work is devoted to elaboration on the idea to use block term decomposition for group data analysis and to raise the possibility of modelling group activity with (Lr, 1) and Tucker blocks. A new generalization of block tensor decomposition was considered in application to group data analysis. Suggested approach was…
Paper proposes a new framework to improve stability-based bounds in deep learning.
This study proposes methods for multi-step-ahead stock price prediction using decomposition and neural networks.
Singular value decomposition (SVD) is the mathematical basis of principal component analysis (PCA). Together, SVD and PCA are one of the most widely used mathematical formalism/decomposition in machine learning, data mining, pattern recognition, artificial intelligence, computer vision, signal processing, etc. In recen…
Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framew…
We consider deep feedforward neural networks with rectified linear units from a signal processing perspective. In this view, such representations mark the transition from using a single (data-driven) linear representation to utilizing a large collection of affine linear representations tailored to particular regions of…
New method interprets ranked data on permutahedron graph.
New neural network extracts signal components and their IFs from non-uniform samples.
Proposes a new method for uncertainty estimation in neural networks.
Derives a primal-dual MLSVD formulation for multilinear data.
Privacy subsidy found in market trading with noisy direction signals.
TSL learns separable models to avoid signal cancellation and off-support extrapolation.
Divergence functions play a key role as to measure the discrepancy between two points in the field of machine learning, statistics and signal processing. Well-known divergences are the Bregman divergences, the Jensen divergences and the f-divergences. In this paper, we show that the symmetric Bregman divergence can be …
We propose a new framework for single-channel source separation that lies between the fully supervised and unsupervised setting. Instead of supervision, we provide input features for each source signal and use convex methods to estimate the correlations between these features and the unobserved signal decomposition. We…
Novel approach for estimating joint probability densities using tensor decompositions and dictionaries.
This work is motivated by multimodality breast cancer imaging data, which is quite challenging in that the signals of discrete tumor-associated microvesicles (TMVs) are randomly distributed with heterogeneous patterns. This imposes a significant challenge for conventional imaging regression and dimension reduction mode…
Combining neural networks and multiscale decomposition for financial market analysis.
A parallel algorithm learns efficient Kronecker product dictionaries.
Paper studies nonnegative Tucker decomposition identifiability with sparsity conditions.
Tensors or {\em multi-way arrays} are functions of three or more indices -- similar to matrices (two-way arrays), which are functions of two indices for (row,column). Tensors have a rich history, stretching over almost a century, and touching upon numerous disciplines; but they have only recent…
Graph-Dictionary model for sparse multivariate signal representation.