From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures.
This paper introduces a fast implementation of the power iteration method for subspace tracking, based on an approximation that is less restrictive than the well-known projection approximation. This algorithm, referred to as the fast approximated power iteration (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 designed for both exponential windows and sliding windows. Our numerical simulations show that sliding windows offer a faster tracking response to abrupt signal variations.
T. Vijayakumar, V.Nivedhitha, K.Deeba, and M. Bama. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), 2 (1):
35-43(February 2012)
R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Proceedings of the ACM SIGMOD Int'l Conference on Management of Data, Seattle, Washington, page 94--105. ACM Press, (June 1998)
R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Proceedings of the ACM SIGMOD Int'l Conference on Management of Data, Seattle, Washington, page 94--105. ACM Press, (June 1998)
S. Günnemann, I. Färber, and T. Seidl. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, page 132--140. New York, NY, USA, ACM, (2012)
R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Proceedings of the ACM SIGMOD Int'l Conference on Management of Data, Seattle, Washington, page 94--105. ACM Press, (June 1998)