Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes two new algorithms for tracking the subspace spanned by the principal eigenvectors of the correlation matrix associated with time-domain (i.e., time series) data. The algorithms track the d principal N-dimensional eigenvectors of the data covariance matrix with a complexity of O(Nd/sup 2/), yet they have performance comparable with algorithms having O(N/sup 2/d) complexity. The computation reduction is achieved by exploiting the shift-invariance property of temporal data covariance matrices. Experiments are used to compare our algorithms with other ll-known approaches of similar and/or lor complexity. Our new algorithms are shown to outperform the subset of the general approaches having the same complexity. The new algorithms are useful for applications such as subspace-based speech enhancement and low-rank adaptive filtering.
['Carlos E. Davila']
I am writing a research paper on a idea Efficient, high performance, subspace tracking for time-domain data
[{"from": "human", "value": "I am writing a research paper on a idea Efficient, high performance, subspace tracking for time-domain data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes two new algorithms for tracking the subspace spanned by the principal eigenvectors of the correlation matrix associated with time-domain (i.e., time series) data. The algorithms track the d principal N-dimensional eigenvectors of the data covariance matrix with a complexity of O(Nd/sup 2/), yet they have performance comparable with algorithms having O(N/sup 2/d) complexity. The computation reduction is achieved by exploiting the shift-invariance property of temporal data covariance matrices. Experiments are used to compare our algorithms with other ll-known approaches of similar and/or lor complexity. Our new algorithms are shown to outperform the subset of the general approaches having the same complexity. The new algorithms are useful for applications such as subspace-based speech enhancement and low-rank adaptive filtering."}]