2005
DOI: 10.1109/tsp.2005.850378
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Fast approximated power iteration subspace tracking

Abstract: Abstract-This paper introduces a fast implementation of the power iteration method for subspace tracking, based on an approximation less restrictive than the well known projection approximation. This algorithm, referred to as the fast API method, guarantees the orthonormality of the subspace weighting matrix at each iteration. Moreover, it outperforms many subspace trackers related to the power iteration method, such as PAST, NIC, NP3 and OPAST, while having the same computational complexity. The API method is… Show more

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Cited by 167 publications
(135 citation statements)
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“…The detailed theory description of the FAPI algorithm was introduced in reference [15,18], so we will not discuss the derivation of this method again. Table 1 shows the procedures of FAPI.…”
Section: Review Of the Fapi Algorithmmentioning
confidence: 99%
“…The detailed theory description of the FAPI algorithm was introduced in reference [15,18], so we will not discuss the derivation of this method again. Table 1 shows the procedures of FAPI.…”
Section: Review Of the Fapi Algorithmmentioning
confidence: 99%
“…In [6], algorithms are classified by desired output (signal subspace or noise subspace), computational complexity, the number of parameters to be tuned, and whether or not the computed basis vector estimates are orthogonal at each iteration. Based on these criteria, we selected the Fast Approximate Power Iteration (FAPI) algorithm [1]. This approach provides orthonormal basis vector estimates at every iteration and is very efficient computationally, with complexity O(NR), where N is the dimensionality of the data and R the size of the desired subspace.…”
Section: -Adaptive Subspace Projectionmentioning
confidence: 99%
“…At every iteration, the R current basis vectors are stored in an N×R weighting matrix, denoted W(t), which contains the estimates that are current once the observation at time t has been incorporated. The steps by which W(t-1) is updated to produce W(t), with decay parameter β 1 , are clearly set out in [1].…”
Section: -Adaptive Subspace Projectionmentioning
confidence: 99%
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