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- Yang Sup Song, Peter J. Schreier, David Ramírez, Tanuj Hasija
- Signal Processing
- 2016

This paper is concerned with the analysis of correlation between two high-dimensional data sets when there are only few correlated signal components but the number of samples is very small, possibly much smaller than the dimensions of the data. In such a scenario, a principal component analysis (PCA) rank-reduction preprocessing step is commonly performed… (More)

- Yang Sup Song, Tanuj Hasija, Peter J. Schreier, David Ramírez
- 2016 24th European Signal Processing Conference…
- 2016

This paper presents a detection scheme for determining the number of signals that are correlated across multiple data sets when the sample size is small compared to the dimensions of the data sets. To accommodate the sample-poor regime, we decouple the problem into several independent two-channel order-estimation problems that may be solved separately by a… (More)

- Tanuj Hasija, Yang Sup Song, Peter J. Schreier, David Ramírez
- 2016 IEEE Statistical Signal Processing Workshop…
- 2016

This paper addresses the problem of detecting the number of signals correlated across multiple data sets with small sample support. While there have been studies involving two data sets, the problem with more than two data sets has been less explored. In this work, a rank-reduced hypothesis test for more than two data sets is presented for scenarios where… (More)

- Tanuj Hasija, Yang Sup Song, Peter J. Schreier, David Ramírez
- ACSSC
- 2016

We present a scheme for determining the number of signals common to or correlated across multiple data sets. Handling multiple data sets is challenging due to the different possible correlation structures. For two data sets, the signals are either correlated or uncorrelated between the data sets. For multiple data sets, however, there are numerous… (More)

- Tanuj Hasija, Christian Lameiro, Peter J. Schreier
- IEEE Signal Processing Letters
- 2017

A complex-valued signal is improper if it is correlated with its complex conjugate. The dimension of the improper signal subspace, i.e., the number of improper components in a complex-valued measurement, is an important parameter and is unknown in most of the applications. In this letter, we introduce two approaches to estimate this dimension: one based on… (More)

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