A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is `application/pdf`

.

## Filters

##
###
Random hyperplane projection using derived dimensions

2010
*
Proceedings of the Ninth ACM International Workshop on Data Engineering for Wireless and Mobile Access - MobiDE '10
*

In our work we investigate a particular form of LSH, termed

doi:10.1145/1850822.1850827
dblp:conf/mobide/GeorgoulasK10
fatcat:ican5c3dbzfynldaddlrduhcce
*Random**Hyperplane**Projection*(RHP). RHP is a data agnostic model that works for arbitrary data sets. ... Our experimental evaluation*using*several real datasets demonstrates that our proposed scheme outperforms the existing RHP algorithm providing up to three times more accurate similarity computations*using*... Handling Evolving Data Sets The proposed RHP (n, m) framework makes*use*of precomputed statistics in order to boost the accuracy of the standard*random**hyperplane**projection*scheme. ...##
###
Approximate Nearest Subspace Search with Applications to Pattern Recognition

2007
*
2007 IEEE Conference on Computer Vision and Pattern Recognition
*

Further speedup may be achieved by

doi:10.1109/cvpr.2007.383201
dblp:conf/cvpr/BasriHZ07
fatcat:yaffuf6edvbzjjkxxwf7lskzmy
*using**random**projections*to lower the dimensionality of the problem. ... Linear and affine subspaces are commonly*used*to describe appearance of objects under different lighting, viewpoint, articulation, and identity. ...*Random**projections*are commonly*used*in ANN searches whenever d log n. ...##
###
Generic LSH Families for the Angular Distance Based on Johnson-Lindenstrauss Projections and Feature Hashing LSH
[article]

2017
*
arXiv
*
pre-print

Our tests

arXiv:1704.04684v1
fatcat:hmi6ggvsvvhjbb26vxhovxzawy
*using*real datasets show that the proposed LSH functions work well for the euclidean distance. ... We show that feature hashing is a valid J-L*projection*and propose two new LSH families based on feature hashing. ... This means FH is*used*to*project*from d to m*dimensions*and then a*random*rotation is*used*from m to d*dimensions*. ...##
###
Expander-like Codes based on Finite Projective Geometry
[article]

2012
*
arXiv
*
pre-print

The code is based on a bipartite graph

arXiv:1209.3460v1
fatcat:g7a4c5idhjaqhfsxqbx3l7xdty
*derived*from the subsumption relations of finite*projective*geometry, and Reed-Solomon codes as component codes. ... By*derivation*of geometric bounds rather than eigenvalue bounds, it has been proved that for practical values of the code rate, the*random*error correction capability of our codes is much better than those ... This bipartite graph is*derived*from point-*hyperplane*incidence relations of*projective*spaces of higher*dimensions*than those suggested by [1] . ...##
###
The Overlay of Minimization Diagrams in a Randomized Incremental Construction

2011
*
Discrete & Computational Geometry
*

In a

doi:10.1007/s00454-010-9324-6
fatcat:oe63l7b3uvaevds3e4nszr6fli
*randomized*incremental construction of the minimization diagram of a collection of n*hyperplanes*in R d , the*hyperplanes*are inserted one by one, in a*random*order, and the minimization diagram is ... E H and M H can be constructed*using*a standard*randomized*incremental algorithm [9] . In this approach, we draw a*random*permutation of H, and insert the*hyperplanes*of H one by one in this order. ... The minimization diagram M H of H is the*projection*of E H onto the*hyperplane*x d = 0. ...##
###
Maximum Margin Discriminant Projections For Facial Expression Recognition

2013
*
Zenodo
*

facial images

doi:10.5281/zenodo.43668
fatcat:a74jtyfpyfbefjexmbijinvzy4
*using**random**projections*. ... MMDP algorithm directly operates on the*random*features extracted*using*an orthogonal Gaussian*random**projection*matrix and*derives*an optimal*projection*matrix such that the separating margin between ...##
###
Solving Linear SVMs with Multiple 1D Projections

2014
*
Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14
*

Our solution adapts on methodologies from

doi:10.1145/2661829.2661994
dblp:conf/cikm/SchneiderBV14
fatcat:s3or76ldzvhdzdwt4jwh3khmfy
*random**projections*, exponential search, and coordinate descent. ... We present a new methodology for solving linear Support Vector Machines (SVMs) that capitalizes on multiple 1D*projections*. ...*Using**Random**Projections*: In our approach we*use*multiple 1D*random**projections*. ...##
###
Learning Kernel Perceptrons on Noisy Data Using Random Projections
[chapter]

2007
*
Lecture Notes in Computer Science
*

Our proposed approach relies on the combination of the technique of

doi:10.1007/978-3-540-75225-7_27
fatcat:jlrodqj4i5gzxjjmwnfw5qdo2y
*random*or deterministic*projections*with a classification noise tolerant perceptron learning algorithm that assumes distributions defined ... Provided a sufficient separation margin characterizes the problem, this strategy makes it possible to envision the learning from a noisy distribution in any separable Hilbert space, regardless of its*dimension*... KPCA (almost) always requires a smaller*dimension*of*projection*than KGS and*random**projection*. ...##
###
Polytopes, Lattices, and Spherical Codes for the Nearest Neighbor Problem

2020
*
International Colloquium on Automata, Languages and Programming
*

We study locality-sensitive hash methods for the nearest neighbor problem for the angular distance, focusing on the approach of first

doi:10.4230/lipics.icalp.2020.76
dblp:conf/icalp/Laarhoven20
fatcat:pk465duvebaoldcu4nikztjjki
*projecting*down onto a*random*low-dimensional subspace, and then partitioning ... We provide lower bounds based on spherical caps, and we predict that in higher*dimensions*, larger spherical codes exist which outperform orthoplices in terms of the query exponent, and we argue why*using*... than*using**random*Gaussian*projection*matrices. ...##
###
A Randomized Algorithm for Large Scale Support Vector Learning

2007
*
Neural Information Processing Systems
*

The key contribution of the paper is to show that, by

dblp:conf/nips/KumarBH07
fatcat:adg5pryrejgalek63txgrnw5hu
*using*ideas*random**projections*, the minimal number of support vectors required to solve almost separable classification problems, such that the solution ... This paper investigates the application of*randomized*algorithms for large scale SVM learning. ... d,*using*ideas from*random**projections*. ...##
###
APPROXIMATING CENTER POINTS WITH ITERATIVE RADON POINTS

1996
*
International journal of computational geometry and applications
*

Our algorithm has been

doi:10.1142/s021819599600023x
fatcat:tp4mz4vz2bf6rf2oujhinqxnfi
*used*in mesh partitioning methods and can be*used*in the construction of high breakdown estimators for multivariate datasets in statistics. ... Introduction A center point of a set P of n points in IR d is a point c of IR d such that every*hyperplane*passing through c partitions P into two subsets each of size at most nd/(d + 1) [9, 27] . ... This algorithm is analyzed first in one*dimension*, in Section 4, then in general in Section 5. Section 6 discusses the*use*of*random*sampling to eliminate dependence on the number of input points. ...##
###
The L1-norm best-fit hyperplane problem

2013
*
Applied Mathematics Letters
*

We formalize an algorithm for solving the L 1 -norm best-fit

doi:10.1016/j.aml.2012.03.031
pmid:23024460
pmcid:PMC3459998
fatcat:dna2yuvvlfgondam7bgny4sngy
*hyperplane*problem*derived**using*first principles and geometric insights about L 1*projection*and L 1 regression. ... This analysis of the L 1 -norm best-fit*hyperplane*problem makes the procedure accessible to applications in areas such as location theory, computer vision, and multivariate statistics. ... It is*derived*and motivated directly from fundamental geometric insights about L 1*projections*. ...##
###
Decomposing arrangements of hyperplanes: VC-dimension, combinatorial dimension, and point location
[article]

2017
*
arXiv
*
pre-print

Our main application is to point location in an arrangement of $n$

arXiv:1712.02913v1
fatcat:rfd5esufrbftfnpxf4l4s22bb4
*hyperplanes*is $\Re^d$, in which we show that the query cost in Meiser's algorithm can be improved if one*uses*vertical decomposition ... We discuss the tradeoff between query cost and storage (in both approaches, the one*using*bottom-vertex trinagulation and the one*using*vertical decomposition). ... Instead, we*use*the smaller combinatorial*dimension*, via the Clarkson-Shor analysis technique. See the table in Figure 5 .5 for a summary of the various bounds, as*derived*earlier in this paper. ...##
###
Maximum Margin Projection Subspace Learning for Visual Data Analysis

2014
*
IEEE Transactions on Image Processing
*

The proposed method is an iterative alternate optimization algorithm that computes the maximum margin

doi:10.1109/tip.2014.2348868
pmid:25148664
fatcat:vtmo3xxupjbjtdxivxhuwazp3y
*projections*exploiting the separating*hyperplanes*obtained from training a support vector machine classifier ... The proposed method called maximum margin*projection*pursuit, aims to identify a low dimensional*projection*subspace, where samples form classes that are better discriminated, i.e., are separated with ... The resulting from each facial image 48, 000D feature vectors were*used*in order to train MMPP and obtain the*projection*matrix R of*dimensions*100 × 48, 000. ...##
###
Intrinsic Volumes of Polyhedral Cones: A Combinatorial Perspective

2017
*
Discrete & Computational Geometry
*

Direct

doi:10.1007/s00454-017-9904-9
fatcat:ye2v4s4qhrg3bjbka7seh63txy
*derivations*of the general Steiner formula, the conic analogues of the Brianchon-Gram-Euler and the Gauss-Bonnet relations, and the principal kinematic formula are given. ... In addition, a connection between the characteristic polynomial of a*hyperplane*arrangement and the intrinsic volumes of the regions of the arrangement, due to Klivans and Swartz, is generalized and some ... The work in this paper was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (*Project*No. CityU 21203315). ...
« Previous

*Showing results 1 — 15 out of 20,014 results*