Balas K. Natarajan

Learn More
The following problem is considered: given a matrix A in Rm’’, (m rows and n columns), a vector b in Rm, and 6 > 0, compute a vector x satisfying IIAx bl[2 <_ 6 if such exists, such that x has the fewest number of non-zero entries over all such vectors. It is shown that the problem is NP-hard, but that the well-known greedy heuristic is good in that it(More)
We present an algorithm and a hardware architecture for block-based motion estimation that involves transforming video sequences from a multibit to a one-bit/pixel representation and then applying conventional motion estimation search strategies. This results in substantial reductions in arithmetic and hardware complexity and reduced power consumption,(More)
This paper presents some results on the probabilistic analysis of learning, illustrating the applicability of these results to settings such as connectionist networks. In particular, it concerns the learning of sets and functions from examples and background information. After a formal statement of the problem, some theorems are provided identifying the(More)
This paper deals with the learnability of Boolean functions. An intuitively appealing notion of dimensionality is developed and used to identify the most general class of Boolean function families that are learnable from polynomially many positive examples with one-sided error. It is then argued that although bounded DNF expressions lie outside this class,(More)
Preprocessing of image and video sequences with spatial filtering techniques usually improves the image quality and compressibility. We present a block-based, nonlinear filtering algorithm based on singular value decomposition and compression-based filtering. Experiments show that the proposed filter preserves edge details and can significantly improve the(More)
This paper explores a new direction in the formal theory of learning learning in the sense of improving computational efficiency as opposed to concept learning in the sense of Valiant. Specifically, the paper concerns algorithms that leam to solve problems from sample instances of the problems. We develop a general framework for such learning and study the(More)
This paper concerns the problem of assembling composite objects. We study the problem on two fronts; Firstly we study the complexity of deciding the existence of an assembly sequence and show that this is PSPACE-hard in general. Secondly we define a new measure of complexity, one that attempts to measure the minimum number of hands required to assemble a(More)