This paper presents a novel method for pattern recognition problem in terms of linear regression. Normally, patterns from a single-object class lie on a linear subspace. Using this concept, we develop a linear model representing a probe image as a linear combination of class-specific galleries. Linear Regression Classification (LRC) algorithm for pattern recognition belongs to the category of nearest subspace classification. This algorithm is extensively evaluated on several standard digit and English character databases and our own Tamil character database. A comparative study with different databases and methods clearly reflects the efficiency of LRC approach for pattern recognition.
Unstructured data are irregular information with no predefined data model. Streaming data which constantly arrives over time is unstructured, and classifying these data is a tedious task as they lack class labels and get accumulated over time. As the data keeps growing, it becomes difficult to train and create a model from scratch each time. Incremental learning, a self-adaptive algorithm uses the previously learned model information, then learns and accommodates new information from the newly arrived data providing a new model, which avoids the retraining. The incrementally learned knowledge helps to classify the unstructured data. In this paper, we propose a framework CUIL (Classification of Unstructured data using Incremental Learning) which clusters the metadata, assigns a label for each cluster and then creates a model using Extreme Learning Machine (ELM), a feed-forward neural network, incrementally for each batch of data arrived. The proposed framework trains the batches separately, reducing the memory resources, training time significantly and is tested with metadata created for the standard image datasets like MNIST, STL-10, CIFAR-10, Caltech101, and Caltech256. Based on the tabulated results, our proposed work proves to show greater accuracy and efficiency.
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