Results 91 to 100 of about 541,400 (289)
Impact Parameter Analysis of Subspace Clustering
International Journal of Distributed Sensor Networks, 2015 Subspace clustering, which detects all clusters in affine subspaces of a given high dimensional vector space, is used in various applications, including e-business.
Dongjin Lee, Junho Shim
doaj +1 more source
Ion‐Gating Reservoir Computing for Preprocessing‐Free Speech Recognition from Throat Vibrations
Advanced Electronic Materials, EarlyView.This work presents a throat‐mounted mechanoelectric sensor integrated with an ion‐gel/graphene reservoir device for on‐device speech recognition. The system converts raw biomechanical vibrations into rich nonlinear current dynamics, enabling efficient classification through a simple linear readout. The approach highlights a compact and tunable physical‐
Daiki Nishioka +5 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley +1 more source
Robust Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation
, 2016 Low-rank representation (LRR) has been successfully applied in exploring the subspace structures of data. However, in previous LRR-based semi-supervised subspace clustering methods, the label information is not used to guide the affinity matrix ...
Fang, Xiaozhao +9 more
core +1 more source
Advanced Intelligent Discovery, EarlyView.Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez +4 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi +2 more
wiley +1 more source
Noisy Sparse Subspace Clustering
J. Mach. Learn. Res., 2013 Manuscript currently under review at journal of machine learning research.
Yu-Xiang Wang 0003, Huan Xu
openaire +5 more sources
Doubly Stochastic Subspace Clustering
CoRR, 2020 Many state-of-the-art subspace clustering methods follow a two-step process by first constructing an affinity matrix between data points and then applying spectral clustering to this affinity. Most of the research into these methods focuses on the first step of generating the affinity, which often exploits the self-expressive property of linear ...
Derek Lim +2 more
openaire +2 more sources
A Language‐Guided Multimodal Foundation Model for Zero‐Shot and Multi‐Task Brain Signal Analysis
Advanced Intelligent Systems, EarlyView.METIS aligns brain signals with natural‐language instructions to enable zero‐shot and multi‐task brain signal analysis. Pretrained on over 70 000 h of EEG and iEEG recordings, it generalizes across sleep stage classification, epilepsy detection, and neurological disorder diagnosis, providing a scalable foundation model for clinically meaningful brain ...
Mingzhi Chen +3 more
wiley +1 more source
Hierarchical Subspace Clustering [PDF]
, 2007 It is well-known that traditional clustering methods considering all dimensions of the feature space usually fail in terms of efficiency and effectivity when applied to high-dimensional data.
Achtert, Elke
core +1 more source