16028378
Improving the Performance of Infomax Using Statistical Signal Processing Techniques
In this paper, we present a new method that speeds up the convergence of the infomax algorithm proposed by Bell and Sejnowski. One effect of the infomax algorithm is that the 2nd order and 4th order statistical correlations are reintroduced to the signals during the learning process due to the optimization with respect to the complete signal statistics. We show that repetitively forcing 2nd and 4th order correlations to zero speeds up the convergence and improves separating sources with fewer data points.
6741849
Blind Separation of Delayed and Convolved Sources
We address the difficult problem of separating multiple speakers with multiple microphones in a real room. We combine the work of Torkkola and Amari, Cichocki and Yang, to give Natural Gradient information maximisation rules for recurrent (IIR) networks, blindly adjusting delays, separating and deconvolving mixed signals. While they work well on simulated data, these rules fail in real rooms which usually involve non-minimum phase transfer functions, not-invertible using stable IIR filters. An approach that sidesteps this problem is to perform infomax on a feedforward architecture in the frequency domain (Lambert 1996). We demonstrate real-room separation of two natural signals using this approach.
207739442
Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources
An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions. This was achieved by using a simple type of learning rule first derived by Girolami (1997) by choosing negentropy as a projection pursuit index. Parameterized probability distributions that have sub- and supergaussian regimes were used to derive a general learning rule that preserves the simple architecture proposed by Bell and Sejnowski (1995), is optimized using the natural gradient by Amari (1998), and uses the stability analysis of Cardoso and Laheld (1996) to switch between sub- and supergaussian regimes. We demonstrate that the extended infomax algorithm is able to separate 20 sources with a variety of source distributions easily. Applied to high-dimensional data from electroencephalographic recordings, it is effective at separating artifacts such as eye blinks and line noise from weaker electrical signals that arise from sources in the brain.
767826
The Spectral Independent Components of Natural Scenes
We apply independent component analysis (ICA) for learning an efficient color image representation of natural scenes. In the spectra of single pixels, the algorithm was able to find basis functions that had a broadband spectrum similar to natural daylight, as well as basis functions that coincided with the human cone sensitivity response functions. When applied to small image patches, the algorithm found homogeneous basis functions, achromatic basis functions, and basis functions with overall chromatic variation along lines in color space. Our findings suggest that ICAm ay be used to reveal the structure of color information in natural images.
17578293
Representational strategy in the visual cortex
We generalized Daubechies' (see IEEE Transactions on Information Theory, vol.36, no.5, p.961-1004, 1990) results on 1D wavelets to 2D, and elucidated the conditions in which a family of Gabor wavelets will provide a complete representation of an image. We found that the phase space (scale, orientation, x, y) sampling density of the striate cortical simple cells is sufficiently dense to form a tight wavelet frame. This tight frame approximates the continuous wavelet transform, and its redundancy allows high resolution information to be represented in the low-resolution firings of the simple cells. We presented some experimental results to demonstrate this idea.<<ETX>>
16239260
Hybrid local polynomial wavelet shrinkage: wavelet regression with automatic boundary adjustment
An usual assumption underlying the use of wavelet shrinkage is that the regression function is assumed to be either periodic or symmetric. However, such an assumption is not always realistic. This paper proposes an effective method for correcting the boundary bias introduced by the inappropriateness of such periodic or symmetric assumption. The idea is to combine wavelet shrinkage with local polynomial regression, where the latter regression technique is known to possess excellent boundary properties. Simulation results from both the univariate and bivariate settings provide strong evidence that the proposed method is extremely effective in terms of correcting boundary bias.
12361441
Computer-aided prototype system for nose surgery
Rhinoplasty, or surgery to reshape the nose, is one of the most common of all plastic-surgery procedures. Rhinoplasty can enhance a patient's appearance and self-confidence, may also correct a birth defect or injury, or help relieve some breathing problem. In this paper, we present a three-dimensional (3-D) surgical simulation system, which can assist surgeons in planning rhinoplasty procedures. This system employs computer graphics and image-processing techniques for the simulation of a rhinoplasty. Although the presented algorithms themselves are not new, the proposed system exploits the new idea to apply 3-D morphing for rhinoplasty, and simulation results are useful for the physicians. According to patients' expectation of what they would like their noses to look like, our system simulates expected results. Our tools provide quantitative measurements of a nose structure. Using these quantitative results, surgeons can arrange appropriate preoperative plans for patients. Finally, experimental results and experiences are reported to evaluate the usefulness of the proposed system.
44493552
Adaptation of the temporal receptive fields of macaque V1 neurons
Abstract Traditional view of macaque V1 neurons is that they are static spatiotemporal filters. This study shows that V1 neurons’ temporal receptive fields (kernels) are dynamic and adaptive. In order to study the adaptation of the kernels to a variety of stimuli, we developed a regression approach to recover kernels using naturalistic stimuli. Using this technique, we studied how the kernels of the V1 neurons in macaque monkeys adapt to the frequency bandwidth of naturalistic signals. We found that the temporal frequency tunings and the gains of the receptive fields of the neurons change according to the statistical context of the stimulus environment.
76211
Negative weights make adversaries stronger
The quantum adversary method is one of the most successful techniques for proving lower bounds on quantum query complexity. It gives optimal lower bounds for many problems, has application to classical complexity in formula size lower bounds, and is versatile with equivalent formulations interms of weight schemes, eigen values, and Kolmogorov complexity. All these formulations rely on the principlethat if an algorithm successfully computes a function then, in particular, itis able to distinguish between inputs which map to different values.
 We present a stronger version of the adversary method which goes beyond this principle to make explicit use of the stronger condition that the algorithm actually computes the function. This new method, which we call ADV+-, has all the advantages ofthe old: it is a lower bound on bounded-error quantum query complexity, its square is a lower bound on formula size, and it behaves well with respect tofunction composition. Moreover ADV+- is always at least as large as the adversary method ADV, and we show an example of a monotone function forwhich ADV+-(f)=Omega(ADV(f)1.098). We also give examples showing that ADV+- does not face limitations of ADV like the certificate complexity barrier and the property testing barrier.