Maximal Similarity Embedding

Abstract

In recent times the dimensionality reduction technique has been widely exploited in pattern recognition and data mining. The global linear algorithms characterize the local sampling information, thereby making it superior to Principal Component Analysis (PCA). However, these algorithms are all inefficient for extracting the local data feature, which leads to incomplete learning. A new global linear algorithm is proposed in this paper, which is named Maximal Similarity Embedding (MSE). The preserving local feature of this new algorithm makes it distinct from most other methods. The MSE algorithm utilizes the Cosine Metric to describe the geometric characteristics of neighborhood and thus seeks to maximize the global similarity for dimensionality reduction. This new proposal method is robust for sparse dataset and naturally helps in avoiding the problem of small sample size cases. Extensive experiments have been performed on both synthetic and real-world images to prove the efficiency of the MSE algorithm.