We provide an overview of our work in applying unsupervised topic and authortopic models based on Latent Dirichlet Allocation (LDA) to the problem of mining large software repositories at multiple levels of granularity. Our approaches allow us to automatically discover the topics embedded in code and extract documenttopic and author-topic distributions. In addition to serving as a convenient su...

The availability of whole genome sequence data has facilitated the development of high-throughput technologies for monitoring biological signals on a genomic scale. The revolutionary microarray technology, first introduced in 1995 (Schena et al., 1995), is now one of the most valuable techniques for global gene expression profiling. Other high-throughput genomic technologies, such as Serial Ana...

Unsupervised Analysis of Structured Human Artifacts by Taylor Berg-Kirkpatrick Doctor of Philosophy in Computer Science University of California, Berkeley Professor Dan Klein, Chair The presence of hidden structure in human data—including natural language but also sources like music, historical documents, and other complex artifacts—makes this data extremely difficult to analyze. In this thesis...

Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find ...

The goal of object level annotation is to locate and identify instances of an object category within an image. Nowadays, Most of the current object level annotation systems annotate the object according to the visual appearance in the image. Recognizing an object in an image based visual appearance yield ambiguity in object detection due to appearance confusion for example “sky” object may be a...

Recently text-based sentiment prediction has been extensively studied, while image-centric sentiment analysis receives much less attention. In this paper, we study the problem of understanding human sentiments from large-scale social media images, considering both visual content and contextual information, such as comments on the images, captions, etc. The challenge of this problem lies in the ...

Remote sensing is a promising technology that finds as diverse applications as defence, urbanplanning, healthcare, and environmental management. Collecting countrywide statistics of cropyield is one of the main tasks of remote sensing. Acquiring and processing very high-resolution(VHR) satellite images are means accomplishing this task. Processing these remotely sensed(RS) image...

in this paper, an unsupervised classification method using spatial contextual information for polarimetric sar (polsar) image classification is proposed. first, an unsupervised classification based on 2d h/▁α plane was performed, using cloude/pottier target decomposition algorithm. in order to compute the initial values of the cluster centers and hence a rapid convergence of the algorithm, the ...

While classical approaches to unsupervised morphology acquisition often rely on metrics based on information theory for identifying morphemes, we describe a novel approach relying on the notion of formal analogy. A formal analogy is a relation between four forms, such as: reader is to doer as reading is to doing. Our assumption is that formal analogies identify pairs of morphologically related ...

Huge volumes of opinion-rich data is user-generated in social media at an unprecedented rate, easing the analysis of individual and public sentiments. Sentiment analysis has shown to be useful in probing and understanding emotions, expressions and attitudes in the text. However, the distinct characteristics of social media data present challenges to traditional sentiment analysis. First, social...