Learning algorithm based on dynamic incremental dictionary update

Abstract

The invention discloses a learning algorithm based on dynamic incremental dictionary update. The learning algorithm based on dynamic incremental dictionary update comprises the following steps of selecting a pre-training sample set, initializing an initial dictionary, and confirming atom numbers m requiring to be increased; on the basis of an OMP (Orthogonal Matching Pursuit) algorithm, using the initial dictionary to carry out sparse representation on input samples, and obtaining an initial sparse coefficient matrix; calculating a residual error after representation, when the residual error is larger than a predefined threshold, adding m atoms in the initial dictionary, and on the basis of an information entropy, initiating the m atoms; adding the initiated m atoms into the initial dictionary, obtaining a new dictionary matrix, and utilizing the new dictionary matrix to carry out sparse decomposition on the input samples; on the basis of the input samples subjected to sparse decomposition, utilizing a K-SVD algorithm to update the incremental atoms, confirming the incremental atom with the minimum error, carrying out decorrelation on the incremental atoms, and outputting a final dictionary when all the samples are trained. The learning algorithm based on dynamic incremental dictionary update has the beneficial effect that a more effective and more sparse representation can be carried out on a remote sensing data set with a large size.

Description

technical field [0001] The invention relates to a sparse expression technology for massive remote sensing data, in particular to a learning algorithm based on dynamic incremental dictionary update. Background technique [0002] In recent years, the sparse expression of signals has attracted the attention of many researchers. The application range of sparse expression is also very wide, including data compression, feature extraction, etc.; sparse expression refers to training an over-complete dictionary, which is composed of multiple atoms The signal is expressed as a linear combination of these atoms; it mainly includes two steps: sparse representation and dictionary learning, and the difference in the dictionary learning process is also an important factor to distinguish different algorithms; there are two main types of dictionaries: parsing dictionary and non- Analytical dictionaries, due to the fixed atoms of analytical dictionaries, cannot guarantee the sparsity after de...

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Problems solved by technology

[0003] However, classic dictionary learning algorithms, such as the K-SVD algorithm, need to input all the training sample sets at one time. When the size of the training data expands, the samples will no longer be able to be input for training at one time. Obviously, the traditional sparse expression algorithm Insufficient in the sparse representation of big data

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