Abstract
Sparse Representation Classification (SRC) has performed well in the field of image analysis and speaker identification. In this thesis, we applied SRC in single and mixed odor recognition. First, we chose 20 kinds of odor sources and built an SRC-based algorithm to recognize them. Then, we produced 18 kinds of mixed odors by mixing two of the 20 kinds of odor sources. We attempted to recognize the mixed odors by learning from the training data set which consists of single odor data. A mixed odor could be recognized as a linear combination of the single odors by using SRC. Before performing odor recognition, we detected whether the sample was a single odor or a mixed odor with >70% accuracy using an SR-based method. Then, odors were analyzed by the following steps: data preprocessing, dimension reduction, classification and recognition, respectively. For data preprocessing, various methods were applied to compress and normalize the raw data. Afterwards, dimension reduction was achieved via feature selection and feature extraction. We used Sequential Backward Selection (SBS) for feature selection, and Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for feature extraction. For data classification, we applied K-Nearest Neighbor Classification (KNNC) and SRC. For recognition, we used multiclass classification for single odors identification, and compared the results of mixed odor identification produced by a multiclass algorithm and a multi-label algorithm. The mixed-odor dataset consisted of 18 pairs of odors, so there were 36 targets to identify. Results show that, as many as 14 targets could be successfully identified.