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ICASSP
2011
IEEE
13 years 3 months ago
Online performance guarantees for sparse recovery
A K∗ -sparse vector x∗ ∈ RN produces measurements via linear dimensionality reduction as u = Φx∗ + n, where Φ ∈ RM×N (M < N), and n ∈ RM consists of independent ...
Raja Giryes, Volkan Cevher
ICASSP
2011
IEEE
13 years 3 months ago
Compressed classification of observation sets with linear subspace embeddings
We consider the problem of classification of a pattern from multiple compressed observations that are collected in a sensor network. In particular, we exploit the properties of r...
Dorina Thanou, Pascal Frossard
PAMI
2008
162views more  PAMI 2008»
13 years 11 months ago
Dimensionality Reduction of Clustered Data Sets
We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution...
Guido Sanguinetti
NIPS
2003
14 years 24 days ago
Linear Dependent Dimensionality Reduction
We formulate linear dimensionality reduction as a semi-parametric estimation problem, enabling us to study its asymptotic behavior. We generalize the problem beyond additive Gauss...
Nathan Srebro, Tommi Jaakkola
NIPS
2007
14 years 26 days ago
Random Projections for Manifold Learning
We propose a novel method for linear dimensionality reduction of manifold modeled data. First, we show that with a small number M of random projections of sample points in RN belo...
Chinmay Hegde, Michael B. Wakin, Richard G. Barani...
CIARP
2006
Springer
14 years 3 months ago
A New Approach to Multi-class Linear Dimensionality Reduction
Linear dimensionality reduction (LDR) is quite important in pattern recognition due to its efficiency and low computational complexity. In this paper, we extend the two-class Chern...
Luis Rueda, Myriam Herrera
CIARP
2006
Springer
14 years 3 months ago
A Theoretical Comparison of Two Linear Dimensionality Reduction Techniques
Abstract. A theoretical analysis for comparing two linear dimensionality reduction (LDR) techniques, namely Fisher's discriminant (FD) and Loog-Duin (LD) dimensionality reduci...
Luis Rueda, Myriam Herrera
AI
2006
Springer
14 years 3 months ago
On the Performance of Chernoff-Distance-Based Linear Dimensionality Reduction Techniques
Abstract. We present a performance analysis of three linear dimensionality reduction techniques: Fisher's discriminant analysis (FDA), and two methods introduced recently base...
Mohammed Liakat Ali, Luis Rueda, Myriam Herrera