Yes. Christopher Baker has implemented his incremental SVD method in a MATLAB package called IncPACK. It implements methods that are described in his thesis. A brief discussion of why Brand's algorithm tends to accumulate error can be found in a 2012 paper by Baker, et al. A related method by Chahlaoui, et al discusses error bounds on the left singular subspace and the singular values.

I've already mentioned these points in the comments on Stephen's answer, but it bears repeating that the methods by both Baker and by Chahlaoui scale as $O(mnk + nk^{3})$ for a truncated rank-$k$ SVD of an $m$ by $n$ matrix. For low-rank approximations, the $mnk$ term dominates and, depending on the algorithm variant, has a leading constant that is usually between 8 and 12.

Like Stephen's answer, Chahlaoui's algorithm starts with a QR factorization. Stephen's answer will work for calculating left singular vectors, but a dense SVD of the $R$ matrix would have superlinear complexity in $m$ and $n$ prior to truncation (it would be $O(mn^{2})$), which would probably reduce efficiency, but be more accurate.

For what it's worth, I've implemented Brand's algorithm myself, and it's somewhat sensitive to the inner product tolerance used for rank truncation. I haven't used Baker's package, but I believe it would be better, because error estimates exist for Baker's algorithm (or one closely related) and not Brand's algorithm, and because the rank truncation tolerance for Baker's algorithm is on singular values, not inner products.

Yes. Christopher Baker has implemented his incremental SVD method in a MATLAB package called IncPACK (archived on GitHub, within the imtsl project). It implements methods that are described in his master's thesis. A brief discussion of why Brand's algorithm tends to accumulate error can be found in a 2012 paper by Baker, et al. A related method by Chahlaoui, et al discusses error bounds on the left singular subspace and the singular values.

I've already mentioned these points in the comments on Stephen's answer, but it bears repeating that the methods by both Baker and by Chahlaoui scale as $O(mnk + nk^{3})$ for a truncated rank-$k$ SVD of an $m$ by $n$ matrix. For low-rank approximations, the $mnk$ term dominates and, depending on the algorithm variant, has a leading constant that is usually between 8 and 12.

Like Stephen's answer, Chahlaoui's algorithm starts with a QR factorization. Stephen's answer will work for calculating left singular vectors, but a dense SVD of the $R$ matrix would have superlinear complexity in $m$ and $n$ prior to truncation (it would be $O(mn^{2})$), which would probably reduce efficiency, but be more accurate.

For what it's worth, I've implemented Brand's algorithm myself, and it's somewhat sensitive to the inner product tolerance used for rank truncation. I haven't used Baker's package, but I believe it would be better, because error estimates exist for Baker's algorithm (or one closely related) and not Brand's algorithm, and because the rank truncation tolerance for Baker's algorithm is on singular values, not inner products.

Yes. Christopher Baker has implemented his incremental SVD method in a MATLAB package called IncPACK. It implements methods that are described in his thesis. A brief discussion of why Brand's algorithm tends to accumulate error can be found in a 2012 paper by Baker, et al. A related method by Chahlaoui, et al discusses error bounds on the left singular subspace and the singular values.

I've already mentioned these points in the comments on Stephen's answer, but it bears repeating that the methods by both Baker and by Chahlaoui scale as $O(mnk + nk^{3})$ for a truncated rank-$k$ SVD of an $m$ by $n$ matrix. For low-rank approximations, the $mnk$ term dominates and, depending on the algorithm variant, has a leading constant that is usually between 8 and 12.

Like Stephen's answer, Chahlaoui's algorithm starts with a QR factorization. Stephen's answer will work for calculating left singular vectors, but a dense SVD of the $R$ matrix would have superlinear complexity in $m$ and $n$ prior to truncation (it would be $O(mn^{2})$, which would probably reduce efficiency, but be more accurate.

For what it's worth, I've implemented Brand's algorithm myself, and it's somewhat sensitive to the inner product tolerance used for rank truncation. I haven't used Baker's package, but I believe it would be better, because error estimates exist for Baker's algorithm (or one closely related) and not Brand's algorithm, and because the rank truncation tolerance for Baker's algorithm is on singular values, not inner products.

Yes. Christopher Baker has implemented his incremental SVD method in a MATLAB package called IncPACK. It implements methods that are described in his thesis. A brief discussion of why Brand's algorithm tends to accumulate error can be found in a 2012 paper by Baker, et al. A related method by Chahlaoui, et al discusses error bounds on the left singular subspace and the singular values.

I've already mentioned these points in the comments on Stephen's answer, but it bears repeating that the methods by both Baker and by Chahlaoui scale as $O(mnk + nk^{3})$ for a truncated rank-$k$ SVD of an $m$ by $n$ matrix. For low-rank approximations, the $mnk$ term dominates and, depending on the algorithm variant, has a leading constant that is usually between 8 and 12.

Like Stephen's answer, Chahlaoui's algorithm starts with a QR factorization. Stephen's answer will work for calculating left singular vectors, but a dense SVD of the $R$ matrix would have superlinear complexity in $m$ and $n$ prior to truncation (it would be $O(mn^{2})$), which would probably reduce efficiency, but be more accurate.

For what it's worth, I've implemented Brand's algorithm myself, and it's somewhat sensitive to the inner product tolerance used for rank truncation. I haven't used Baker's package, but I believe it would be better, because error estimates exist for Baker's algorithm (or one closely related) and not Brand's algorithm, and because the rank truncation tolerance for Baker's algorithm is on singular values, not inner products.