Results 21 to 30 of about 955,306 (131)
Big Data Privacy Preservation Using Principal Component Analysis and Random Projection in Healthcare
Mathematical Problems in Engineering, Volume 2022, Issue 1, 2022., 2022 With the rising usage of technology, a tremendous volume of data is being produced in the current scenario. This data contains a lot of personal data and may be given to third parties throughout the data mining process. Individual privacy is extremely difficult for the data owner to protect.
Ritu Ratra +4 more
wiley +1 more source
Scalable and compact photonic neural chip with low learning‐capability‐loss
Nanophotonics, Volume 11, Issue 2, Page 329-344, January 2022., 2022 Abstract Photonic computation has garnered huge attention due to its great potential to accelerate artificial neural network tasks at much higher clock rate to digital electronic alternatives. Especially, reconfigurable photonic processor consisting of Mach–Zehnder interferometer (MZI) mesh is promising for photonic matrix multiplier.
Ye Tian +6 more
wiley +1 more source
Bulk Johnson-Lindenstrauss Lemmas
CoRR, 2023 For a set $X$ of $N$ points in $\mathbb{R}^D$, the Johnson-Lindenstrauss lemma provides random linear maps that approximately preserve all pairwise distances in $X$ -- up to multiplicative error $(1\pm ε)$ with high probability -- using a target dimension of $O(ε^{-2}\log(N))$.
openaire +3 more sources
Dimensional reduction in complex living systems: Where, why, and how
BioEssays, Volume 43, Issue 9, September 2021., 2021 Underneath the apparent complexity of life lies simplicity, beyond what mathematics predicts. Mathematics shows that any high‐dimensional system can be reliably compressed into low dimensions. But life achieves a much stronger, exquisite reduction of dimension.
Jean‐Pierre Eckmann, Tsvi Tlusty
wiley +1 more source
Random projections: Data perturbation for classification problems
WIREs Computational Statistics, Volume 13, Issue 1, January/February 2021., 2021 Projections determine distributions! Left: bivariate‐dimensional distributions, one uniform on the unit circle (black), the other uniform on the unit disk (blue). Right: the corresponding densities after the projecting into a one‐dimensional (1D) space. In fact, any p‐dimensional distribution is determined by its 1D projections (cf.
Timothy I. Cannings
wiley +1 more source
Terminal Embeddings in Sublinear Time [PDF]
TheoretiCSRecently (Elkin, Filtser, Neiman 2017) introduced the concept of a {\it terminal embedding} from one metric space $(X,d_X)$ to another $(Y,d_Y)$ with a set of designated terminals $T\subset X$. Such an embedding $f$ is said to have distortion $\rho\ge 1$
Yeshwanth Cherapanamjeri, Jelani Nelson
doaj +1 more source
The Johnson-Lindenstrauss lemma is optimal for linear dimensionality reduction
CoRR, 2014 For any $n>1$ and ...
Kasper Green Larsen, Jelani Nelson
openaire +5 more sources
Mathematical Problems in Engineering, Volume 2020, Issue 1, 2020., 2020 Object tracking is a critical research in computer vision and has attracted significant attention over the past few years. However, the traditional object tracking algorithms often suffer from the object drifting problem due to various challenging factors in complex environments such as object occlusion and background clutter.
Si Chen +7 more
wiley +1 more source
A Sparse Johnson-Lindenstrauss Transform using Fast Hashing [PDF]
, 2023 The \emph{Sparse Johnson-Lindenstrauss Transform} of Kane and Nelson (SODA 2012) provides a linear dimensionality-reducing map $A \in \mathbb{R}^{m \times u}$ in $\ell_2$ that preserves distances up to distortion of $1 + \varepsilon$ with probability $1 -
Thorup, Mikkel, Houen, Jakob Bæk Tejs
core +4 more sources
A Comprehensive Survey on Local Differential Privacy
Security and Communication Networks, Volume 2020, Issue 1, 2020., 2020 With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state‐of‐the‐art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In
Xingxing Xiong +5 more
wiley +1 more source