#### Filter Results:

- Full text PDF available (1)

#### Publication Year

2011

2017

- This year (1)
- Last 5 years (6)
- Last 10 years (7)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Nicholas Asendorf, Raj Rao Nadakuditi
- ACSCC
- 2011

- Nicholas Asendorf, Raj Rao Nadakuditi
- ACSSC
- 2015

- Nicholas Asendorf, Raj Tejas Suryaprakash, Raj Rao Nadakuditi
- 2012 IEEE Statistical Signal Processing Workshop…
- 2012

We consider a matched subspace detection problem where a signal vector residing in an unknown low-rank k subspace is to be detected using a subspace estimate obtained from noisy signal-bearing training data with missing entries. The resulting subspace estimate is inaccurate due to limited training data, missing entries, and additive noise. Recent results… (More)

- Nicholas Asendorf, Raj Rao Nadakuditi
- ACSSC
- 2015

- Nicholas Asendorf, Raj Rao Nadakuditi
- ACSSC
- 2015

- Nicholas Asendorf, Raj Rao Nadakuditi
- IEEE Transactions on Signal Processing
- 2013

We analyze the performance of a matched subspace detector (MSD) where the test signal vector is assumed to reside in an unknown, low-rank <i>k</i> subspace that must be estimated from finite, noisy, signal-bearing training data. Under both a stochastic and deterministic model for the test vector, subspace estimation errors due to limited training data… (More)

- Nicholas Asendorf, Raj Rao Nadakuditi
- IEEE Transactions on Information Theory
- 2017

We consider two matrix-valued data sets that are modeled as low-rank-correlated-signal-plus-Gaussian noise. When empirical canonical correlation analysis (CCA) is used to infer these latent correlations, there is a broad regime, where this inference will fail, which was classified by Bao and collaborators in the limit of high dimensionality and sample size.… (More)

- ‹
- 1
- ›